MLOps engineer

24/03/2026

MLOps engineer

חברה חסויה

Location: Tel Aviv-Yafo

Job Type: Full Time

We are looking for a Backend Engineer to join our MLOps team and help build the infrastructure that powers cutting-edge AI models.
In this role, youll manage the end-to-end MLOps lifecycle, designing event-driven systems that handle massive video data and moving compute-intensive, generative models from research to production.
You'll collaborate closely with AI researchers and video-processing teams to ensure our AI services are scalable, reliable, and performant.

Requirements:
6+ years of production-grade Python development experience.
Strong background in distributed systems: Youve built and debugged complex, event-driven architectures (e.g., Kafka, microservices).
Expertise in Data Engineering at scale: Experience building massive data pipelines and architecting Data Lakes (S3) with compute layers like Athena for large-scale analysis.
Deep understanding of the MLOps lifecycle: Experience taking models from training to deployment, including versioning and performance monitoring.
Experience with containerized environments, microservices, and Kubernetes.
Experience with workflow management frameworks (Temporal, Airflow) and asynchronous programming.
Experience with cloud platforms (AWS preferred) and model-serving frameworks (Triton, VLLM/SGLang, Ray Serve).
A love for exploring new tech and the drive to implement modern frameworks that move the needle.

This position is open to all candidates.

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לעוד משרות ומידע על CrowdStrike >

לפני 18 שעות

Sr. MLOps Engineer - Falcon Cloud Security

CrowdStrike

Location: Tel Aviv-Yafo

Job Type: Full Time

CrowdStrike's Data Science Studio is seeking a pioneering Senior MLOps Engineer to establish and lead our MLOps function from the ground up. As the first MLOps engineer in the studio, you will play a foundational role in shaping how we build, deploy, and scale machine learning systems that protect thousands of organizations worldwide.

This is a unique opportunity to define the technical strategy, influence the technology stack, and architect the infrastructure that will power our AI/ML-driven security solutions for years to come.

This role combines strategic vision with hands-on execution. You'll work at the intersection of data science, engineering, and production operations - building production-grade systems that operate at immense scale while collaborating closely with highly technical data scientists and ML engineering teams across CrowdStrike.

What You'll Do:
- Architect MLOps infrastructure from the ground up: Design and implement the foundational MLOps platform, establishing best practices, tooling, and workflows that will scale with our growing data science initiatives
- Define technology strategy: Evaluate, select, and integrate MLOps technologies and platforms that best serve our needs - from experiment tracking and model versioning to deployment pipelines and monitoring systems
- Build production-grade ML pipelines: Develop robust, scalable pipelines for model training, validation, deployment, and monitoring that handle massive data volumes and ensure reliability in production
- Enable data scientist productivity: Create tools, frameworks, and automation that empower data scientists to move quickly from research to production while maintaining high quality and reliability standards
- Establish monitoring and observability: Implement comprehensive monitoring, logging, and alerting systems to ensure ML models perform optimally in production and issues are detected proactively
- Drive MLOps culture and practices: Champion best practices in ML engineering, CI/CD for ML, model governance, and reproducibility across the data science organization
- Collaborate cross-functionally: Partner closely with data scientists to understand their workflows and pain points, and work with ML engineering teams to ensure seamless integration with broader platform capabilities
-Scale for the future: Design systems with scalability, security, and maintainability in mind, anticipating the needs of a rapidly growing ML portfolio

Requirements:
- 6+ years of experience in MLOps, ML engineering, DevOps, or related infrastructure roles with focus on machine learning systems
- Production ML systems expertise: Proven track record of building and operating ML systems at scale in production environments
- Strong infrastructure and automation skills: Deep knowledge of cloud platforms (AWS, Azure, or GCP), containerization (Docker, Kubernetes), and infrastructure-as-code (Terraform, CloudFormation)
- ML pipeline proficiency: Hands-on experience with ML workflow orchestration tools (e.g., Airflow, Kubeflow, MLflow, Metaflow) and building end-to-end ML pipelines
- Programming excellence: Strong coding skills in Python; experience with additional languages is a plus
- CI/CD and DevOps practices: Expertise in building automated deployment pipelines, version control, and modern DevOps methodologies
- Strategic and hands-on balance: Ability to think architecturally about long-term solutions while rolling up your sleeves to implement them
- Collaborative mindset: Excellent communication skills and ability to work effectively with data scientists, engineers, and stakeholders with varying technical backgrounds
- Startup mentality: Comfort with ambiguity and ability to build from scratch in a fast-paced environment

This position is open to all candidates.

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22/03/2026

Senior AI and MLOps Engineer - Security and Networking Research

חברה חסויה

Location: More than one

Job Type: Full Time

We're looking for a Senior AI/MLOps Engineer to join a group that specializes in Security and Networking, and specifically ML, AI and agent development. As a Senior AI/MLOps Engineer, youll build and maintain the infrastructure, tools and processes necessary to support the AI lifecycle in a production environment. You will collaborate closely with data scientists, software engineers, security architects and DevOps teams to ensure smooth deployment, modeling and optimization of AI models. This role involves creative problem solving alongside engineering teams, and is pivotal for the continued success of AI networking security.

What youll be doing:

Developing, improving and optimizing scalable infrastructure for handling and deploying security and networking AI models and agents in production, ensuring high availability, scalability, reproducibility, and performance.

Optimizing AI models and agents for performance, scalability, and resource utilization, considering factors such as latency, efficiency, and cost.

Monitoring and deploying agentic systems, LLMs, and ML models in production.

Designing and implementing frameworks/pipelines for AI training, inference, and experimentation.

Collaborating closely with data scientists, security architects and software engineers to operationalize and deploy AI models and agents, including packaging and integration with existing systems. Participate in developing and reviewing code, design documents, use case reviews, and test plan reviews.

Collaborating with DevOps teams to integrate pipelines and workflows into the CI/CD process, ensuring flawless deployments and rollbacks.

Building and maintaining monitoring and alerting systems to proactively identify and resolve issues relating to quality, performance and infrastructure.

Implementing access controls, authentication mechanisms, and encryption standards for AI models and data.

Documenting guidelines, and standard operating procedures for MLOps/AI processes and sharing knowledge with the wider team.

Develop proof-of-concepts for new features.

Requirements:
What we need to see:

BSc/MSc in CS/CE or related field (or equivalent experience).

Strong background in AI with experience deploying and monitoring AI/ML models, LLMs and agents to production systems at scale, including distributed and multi-node environments - at least 5 years of experience.

Proficiency in programming languages such as Python, Java, or Scala, along with experience in using ML/AI frameworks and libraries (e.g. TensorFlow, PyTorch).

Proficiency in microservices architecture, container orchestration, cloud platforms, and scalable infrastructure for training and inference workloads.

Knowledge of inference optimization techniques.

Understanding of build infrastructure and CI/CD tools and practices (e.g. GitLab, GitHub Actions, Jenkins).

You are detail-oriented and care deeply about robust, well tested, high-performance code in production environments.

You are proactive, take full ownership of your deliverables, have a can-do approach, and excellent communication and collaboration skills, able to work effectively in multifunctional teams.

Ways to stand out from the crowd:

Knowledge of network protocols and Linux internals.

Security and networking background, with knowledge of security protocols, network architectures, firewalls, intrusion detection systems, and other relevant security and networking concepts.

Experience deploying and optimizing generative models and agents.

Knowledge of network security principles and practices.

This position is open to all candidates.

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09/04/2026

Senior ML Engineer - Applied AI Engineering Group

חברה חסויה

Location: Tel Aviv-Yafo

Job Type: Full Time

Required Senior ML Engineer - Applied AI Engineering Group
The Dream Job
It starts with you - an engineer driven to build the ML platform that turns research into reliable, production-grade intelligence. You care about reproducibility, low-friction experimentation, and infrastructure that earns the trust of the scientists and researchers who depend on it daily. You'll architect and ship our ML platform - training pipelines, model serving, feature stores, experiment tracking, and compute orchestration - turning models into production capabilities across cloud and on-prem, including air-gapped deployments. A significant part of the platform supports large language models, with unique challenges across training, evaluation, and inference in mission-critical environments.
If you want to make a meaningful impact, join our mission and build the ML platform that drives Sovereign AI products - this role is for you.
The Dream-Maker Responsibilities
Build and operate ML training infrastructure - distributed training pipelines, compute scheduling, and reproducible experiment workflows that data scientists rely on daily.
Own model serving and inference systems - packaging, deployment, autoscaling, A/B testing, canary rollouts, and latency/cost optimization for production models.
Run feature stores, model registries, and dataset versioning - enabling self-serve feature engineering, model lineage, and reproducible experiments across teams.
Build experiment tracking and evaluation infrastructure - automated evals, comparison dashboards, drift detection, and monitoring that give teams visibility into model behavior and performance.
Build and maintain production pipelines for training, fine-tuning workflows, and serving domain models - owning reliability, reproducibility, and scale.
Build and maintain the monitoring and observability layer - model performance tracking, data and prediction drift detection, data quality validation, and alerting.
Improve performance and cost across the ML stack - training throughput, inference latency, batch vs. real-time tradeoffs, and compute cost management.
Ship shared tooling - libraries, templates, CI/CD for models, IaC, and runbooks - while collaborating across Data Platform, AI, Data Science, Engineering, and DevOps. Own architecture, documentation, and operations end-to-end.

Requirements:
5+ years in software engineering, with 2+ years focused on ML infrastructure, MLOps, or data-intensive systems
Engineering craft - Strong Python, distributed systems design, testing, secure coding, API design, CI/CD discipline, and production ownership.
ML platform & serving - Model serving frameworks (e.g., Triton, TorchServe, vLLM, Ray Serve); model packaging, deployment pipelines, and inference optimization
Training infrastructure - Distributed training pipelines (e.g., frameworks like PyTorch, JAX) experiment orchestration and reproducibility
ML lifecycle tooling - Feature stores, model registries, experiment tracking (e.g., MLflow, Weights & Biases); dataset versioning and lineage
Data pipelines - Building training and inference data pipelines; familiarity with tools like Spark, Airflow/Dagster, and streaming ingestion
Comfortable with AI coding tools like Cursor, Claude Code, or Copilot
Nice to Have:
Experience operating in constrained environments - on-premise, private cloud, or air-gapped deployments
Hands-on experience with simulation environments, synthetic data generation, or reinforcement learning workflows
Platform & infra - Kubernetes, AWS, Terraform or similar IaC, CI/CD, observability, incident response
Hands-on data science or applied ML experience.

This position is open to all candidates.

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8603632

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23/03/2026

Senior Backend Data Engineer

חברה חסויה

Location: Tel Aviv-Yafo

Job Type: Full Time

We are seeking a Senior Backend Engineer - Data Platform to join our expanding team and play a crucial role in designing, building, and maintaining robust and scalable data pipelines and infrastructure. In this role, you will directly enable data-driven decision-making and support the development and deployment of AI/ML products that power Health.

Youll collaborate closely with engineering, product, and data science teams to ensure our data systems are high-quality, resilient, and scalable as we grow. As a Senior Backend Engineer on our Data Platform team, you will drive efforts to deliver reliable, efficient, and consistent data services across the organization. You will also help enable the rapid development and deployment of advanced features, insights, and AI-driven capabilities that improve outcomes for clinicians and clients.

Who are you?
You are a seasoned backend or data engineer with experience working on production-grade ML/AI-powered products. You thrive in fast-paced, high-ownership environments and are passionate about building scalable and reliable systems. You understand the unique requirements of delivering AI/ML features in production, and you are comfortable working with modern technologies in the LLM/RAG ecosystem.
You pride yourself on delivering high-quality solutions quickly, without sacrificing design or reliability. Youre known for your responsiveness, collaborative spirit, and service-oriented mindset-especially when youre on-call and the stakes are high.How will you contribute?
Design, implement, and maintain scalable and reliable data pipelines and backend systems supporting both operational and analytical needs, with a focus on ML/AI product enablement.
Ensure data processing is optimized for speed, efficiency, and fault tolerance, enabling seamless integration with AI/ML workflows and reliable performance across all our Health products.
Monitor and improve uptime, reliability, and observability of our data infrastructure and pipelines.
Build and maintain systems to ensure data quality, consistency, and usability across the organization, enabling advanced analytics and AI solutions.
Work closely with product and engineering teams to deliver new features rapidly and with a high standard of technical excellence.
Drive innovation in how we build, measure, and optimize data features, backend services, and AI product integrations.
Participate in on-call rotations with a service-oriented approach and fast responsiveness.
Lead scalability efforts to support increasing data volumes, expanding AI/ML initiatives, and new product launches.

Requirements:
What qualifications and skills will help you to be successful?
At least 5 years of experience with Python in backend or data engineering roles, designing and operating large-scale data pipelines, backend services, and data infrastructure in production environments.
Hands-on experience working on ML/AI-powered products in production, with strong understanding of requirements for integrating data platforms with AI features.
Familiarity with modern LLM (Large Language Model) and RAG (Retrieval-Augmented Generation) technologies, and experience supporting their deployment or integration.
Familiar with or have worked with these technologies (or alternatives):
Data Processing & Streaming: Apache Spark, DBT, Airflow, Airbyte, Kafka
API Development: FastAPI, micro-service architecture, SFTP
Data Storage: Data Lakehouse architectures, Apache Iceberg, Vector Databases, RDS
ML/AI: ML/LLM libraries and frameworks (such as Gemini, Hugging Face, etc.)
Cloud Infrastructure: AWS stack (S3, Firehose, Lambda, Athena, etc.), Kubernetes (K8s)
Demonstrated ability to optimize performance and ensure high availability, scalability, and reliability of backend/data systems.
Strong foundation in best practices for data quality, governance, security, and observability.

This position is open to all candidates.

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8588707

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31/03/2026

Senior Data Engineer

חברה חסויה

Location: Tel Aviv-Yafo

Job Type: Full Time

This role has been designed as Hybrid with an expectation that you will work on average 2 days per week from our office.

We are looking for a highly skilled Senior Data Engineer with strong architectural expertise to design and evolve our next-generation data platform. You will define the technical vision, build scalable and reliable data systems, and guide the long-term architecture that powers analytics, operational decision-making, and data-driven products across the organization.

This role is both strategic and hands-on. You will evaluate modern data technologies, define engineering best practices, and lead the implementation of robust, high-performance data solutions-including the design, build, and lifecycle management of data pipelines that support batch, streaming, and near-real-time workloads.

What Youll Do

Architecture & Strategy

Own the architecture of our data platform, ensuring scalability, performance, reliability, and security.
Define standards and best practices for data modeling, transformation, orchestration, governance, and lifecycle management.
Evaluate and integrate modern data technologies and frameworks that align with our long-term platform strategy.
Collaborate with engineering and product leadership to shape the technical roadmap.

Engineering & Delivery

Design, build, and manage scalable, resilient data pipelines for batch, streaming, and event-driven workloads.
Develop clean, high-quality data models and schemas to support analytics, BI, operational systems, and ML workflows.
Implement data quality, lineage, observability, and automated testing frameworks.
Build ingestion patterns for APIs, event streams, files, and third-party data sources.
Optimize compute, storage, and transformation layers for performance and cost efficiency.

Leadership & Collaboration

Serve as a senior technical leader and mentor within the data engineering team.
Lead architecture reviews, design discussions, and cross-team engineering initiatives.
Work closely with analysts, data scientists, software engineers, and product owners to define and deliver data solutions.
Communicate architectural decisions and trade-offs to technical and non-technical stakeholders.

Requirements:
What Were Looking For:
6-10+ years of experience in Data Engineering, with demonstrated architectural ownership.
Expert-level experience with Snowflake (mandatory), including performance optimization, data modeling, security, and ecosystem components.
Expert proficiency in SQL and strong Python skills for pipeline development and automation.
Experience with modern orchestration tools (Airflow, Dagster, Prefect, or equivalent).
Strong understanding of ELT/ETL patterns, distributed processing, and data lifecycle management.
Familiarity with streaming/event technologies (Kafka, Kinesis, Pub/Sub, etc.).
Experience implementing data quality, observability, and lineage solutions.
Solid understanding of cloud infrastructure (AWS, GCP, or Azure).
Strong background in DataOps practices: CI/CD, testing, version control, automation.
Proven leadership in driving architectural direction and mentoring engineering teams.

Nice to Have:
Experience with data governance or metadata management tools.
Hands-on experience with DBT, including modeling, testing, documentation, and advanced features.
Exposure to machine learning pipelines, feature stores, or MLOps.
Experience with Terraform, CloudFormation, or other IaC tools.
Background designing systems for high scale, security, or regulated environments.

This position is open to all candidates.

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19/04/2026

Senior Solutions Engineer - Big Data & Data Infrastructure

חברה חסויה

Location: Tel Aviv-Yafo

Job Type: Full Time

We are seeking an experienced Solutions Data Engineer who possess both technical depth and strong interpersonal skills to partner with internal and external teams to develop scalable, flexible, and cutting-edge solutions. Solutions Engineers collaborate with operations and business development to help craft solutions to meet customer business problems.
A Solutions Engineer works to balance various aspects of the project, from safety to design. Additionally, a Solutions Engineer researches advanced technology regarding best practices in the field and seek to find cost-effective solutions.
Job Description:
Were looking for a Solutions Engineer with deep experience in Big Data technologies, real-time data pipelines, and scalable infrastructure-someone whos been delivering critical systems under pressure, and knows what it takes to bring complex data architectures to life. This isnt just about checking boxes on tech stacks-its about solving real-world data problems, collaborating with smart people, and building robust, future-proof solutions.
In this role, youll partner closely with engineering, product, and customers to design and deliver high-impact systems that move, transform, and serve data at scale. Youll help customers architect pipelines that are not only performant and cost-efficient but also easy to operate and evolve.
We want someone whos comfortable switching hats between low-level debugging, high-level architecture, and communicating clearly with stakeholders of all technical levels.
Key Responsibilities:
Build distributed data pipelines using technologies like Kafka, Spark (batch & streaming), Python, Trino, Airflow, and S3-compatible data lakes-designed for scale, modularity, and seamless integration across real-time and batch workloads.
Design, deploy, and troubleshoot hybrid cloud/on-prem environments using Terraform, Docker, Kubernetes, and CI/CD automation tools.
Implement event-driven and serverless workflows with precise control over latency, throughput, and fault tolerance trade-offs.
Create technical guides, architecture docs, and demo pipelines to support onboarding, evangelize best practices, and accelerate adoption across engineering, product, and customer-facing teams.
Integrate data validation, observability tools, and governance directly into the pipeline lifecycle.
Own end-to-end platform lifecycle: ingestion → transformation → storage (Parquet/ORC on S3) → compute layer (Trino/Spark).
Benchmark and tune storage backends (S3/NFS/SMB) and compute layers for throughput, latency, and scalability using production datasets.
Work cross-functionally with R&D to push performance limits across interactive, streaming, and ML-ready analytics workloads.
Operate and debug object store-backed data lake infrastructure, enabling schema-on-read access, high-throughput ingestion, advanced searching strategies, and performance tuning for large-scale workloads.

Requirements:
2-4 years in software / solution or infrastructure engineering, with 2-4 years focused on building / maintaining large-scale data pipelines / storage & database solutions.
Proficiency in Trino, Spark (Structured Streaming & batch) and solid working knowledge of Apache Kafka.
Coding background in Python (must-have); familiarity with Bash and scripting tools is a plus.
Deep understanding of data storage architectures including SQL, NoSQL, and HDFS.
Solid grasp of DevOps practices, including containerization (Docker), orchestration (Kubernetes), and infrastructure provisioning (Terraform).
Experience with distributed systems, stream processing, and event-driven architecture.
Hands-on familiarity with benchmarking and performance profiling for storage systems, databases, and analytics engines.
Excellent communication skills-youll be expected to explain your thinking clearly, guide customer conversations, and collaborate across engineering and product teams.

This position is open to all candidates.

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8616791

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29/03/2026

Data Group Tech Lead, Staff Engineer

חברה חסויה

Location: Tel Aviv-Yafo

Job Type: Full Time

Were looking for an experienced and passionate Data Group Tech Lead, Staff Engineer to join our Data Platform group. As the Groups Tech Lead, youll shape and implement the technical vision and architecture while staying hands-on across three specialized teams: Data Engineering Infra, Machine Learning Platform, and Data Warehouse Engineering, forming the backbone of our data ecosystem.
The groups mission is to build a state-of-the-art Data Platform that drives us toward becoming the most precise and efficient insurance company on the planet. By embracing Data Mesh principles, we create tools that empower teams to own their data while leveraging a robust, self-serve data infrastructure. This approach enables Data Scientists, Analysts, Backend Engineers, and other stakeholders to seamlessly access, analyze, and innovate with reliable, well-modeled, and queryable data, at scale.
We believe three things matter for every role: drive to push through challenges, efficiency that keeps standards high while moving fast, and adaptability that lets you pivot with data and AI insights. These aren't buzzwords, they're how we actually work.
Our AI-first approach isn't just a tagline either. We're building the future of insurance with AI at the center, and we need people who are genuinely excited to learn and grow alongside these tools.
In this role youll:
Technically lead the group by shaping the architecture, guiding design decisions, and ensuring the technical excellence of the Data Platforms three teams
Design and implement data solutions that address both applicative needs and data analysis requirements, creating scalable and efficient access to actionable insights
Drive initiatives in Data Engineering Infra, including building robust ingestion layers, managing streaming ETLs, and guaranteeing data quality, compliance, and platform performance
Develop and maintain the Data Warehouse, integrating data from various sources for optimized querying, analysis, and persistence, supporting informed decision-makingLeverage data modeling and transformations to structure, cleanse, and integrate data, enabling efficient retrieval and strategic insights
Build and enhance the Machine Learning Platform, delivering infrastructure and tools that streamline the work of Data Scientists, enabling them to focus on developing models while benefiting from automation for production deployment, maintenance, and improvements. Support cutting-edge use cases like feature stores, real-time models, point-in-time (PIT) data retrieval, and telematics-based solutions
Collaborate closely with other Staff Engineers across to align on cross-organizational initiatives and technical strategies
Work seamlessly with Data Engineers, Data Scientists, Analysts, Backend Engineers, and Product Managers to deliver impactful solutions
Share knowledge, mentor team members, and champion engineering standards and technical excellence across the organization.

דרישות:
8+ years of experience in data-related roles such as Data Engineer, Data Infrastructure Engineer, BI Engineer, or Machine Learning Platform Engineer, with significant experience in at least two of these areas
A B.Sc. in Computer Science or a related technical field (or equivalent experience)
Extensive expertise in designing and implementing Data Lakes and Data Warehouses, including strong skills in data modeling and building scalable storage solutions
Proven experience in building large-scale data infrastructures, including both batch processing and streaming pipelines
A deep understanding of Machine Learning infrastructure, including tools and frameworks that enable Data Scientists to efficiently develop, deploy, and maintain models in production, an advantage
Proficiency in Python, Pulumi/Terraform, Apache Spark, AWS, Kubernetes (K8s), and Kafka for building scalable, reliable, and high-performing data solutions
Strong knowledge of databases, including SQL (schema design, query optimization) and NoSQ המשרה מיועדת לנשים ולגברים כאחד.

עדכון קורות החיים לפני שליחה

8594845

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אירעה שגיאה בשליחת פרטיך למשרה

14/04/2026

Senior/ Principal/ Sr Principal Engineer (Cortex Cloud)

חברה חסויה

Location: Tel Aviv-Yafo

Job Type: Full Time

As a Senior/ Principal/ Senior Principal Software Engineer at Cortex Cloud, you will serve as a primary technical architect and visionary for our core communication infrastructure. This role is focused on the critical server-side backbone that facilitates high-scale bidirectional communication between our cloud services and client-side applications.
You will be responsible for the architectural integrity of systems that receive massive data inflows from the field and reliably broadcast intelligence back to millions of endpoints. This is a high-impact leadership role requiring a blend of deep technical mastery in distributed systems and the ability to influence technical strategy across the organization.
Key Responsibilities
Architectural Strategy & Vision: Define and drive the multi-year technical roadmap for our server-side communication infrastructure, ensuring the platform remains resilient and performant under extreme load.
High-Scale Communication Infrastructure: Lead the design and implementation of backend systems optimized for receiving high-scale data from client-side apps and distributing data back to a vast ecosystem of endpoints.
Technical Leadership & Influence: Act as a force multiplier by providing technical guidance to multiple engineering teams, aligning them on shared protocols, architectural standards, and communication patterns.
Drive Engineering Excellence: Champion a culture of high engineering rigor, focusing on deep observability, low-latency data distribution, and runtime stability for mission-critical production environments.
Cross-Functional Collaboration: Partner with Product Management, Infrastructure, and Client-Side Engineering teams to evaluate technical trade-offs, mitigate risks, and ensure seamless end-to-end data flow.
Innovation & Prototyping: Spearhead the evaluation of emerging technologies and lead "proof of concept" initiatives for next-generation transport layers and messaging paradigms.
Technical Mentorship: Invest in the growth of Senior and Staff engineers through deep-dive design reviews, code audits, and hands-on pair programming on the most critical paths.
Strategic Customer Engagement: Support the business by leading technical deep dives with strategic customers, translating complex architectural concepts into actionable confidence for our partners.

Requirements:
5+/ 8+/10+ years of software engineering experience with a proven track record of delivering robust, high-scale distributed systems.
Server-Side Mastery: Deep expertise in systems-level programming and modern backend languages (e.g., Go, Python) with a focus on building scalable server-side infrastructure.
Cloud Native Foundations: Extensive experience designing, deploying, and operating large-scale architectures on GCP, AWS, or Azure, including strong knowledge of Kubernetes, Docker and Helm.
Bidirectional Data Flow: Proven ability to architect systems that handle high-concurrency data ingestion and wide-scale data distribution/broadcasting.
Systemic Problem Solving: Demonstrated experience in profiling, debugging, and optimizing complex distributed systems to eliminate performance bottlenecks.
Influence & Communication: Exceptional ability to communicate complex technical concepts to both highly technical peers and non-technical stakeholders.
Preferred Qualifications
Data Platform Expertise: Familiarity with architecting solutions using large-scale data platforms such as BigQuery, MongoDB, and MySQL.
High-Performance Caching: Hands-on experience with in-memory data stores and acceleration technologies like Redis, Dragonfly, or similar high-throughput caching layers.
Event-Driven Architecture: Deep understanding of Event-Driven systems and asynchronous messaging patterns to ensure decoupled and scalable service interactions.
Modern Tooling: Experience leveraging AI-assisted development tools (Gemini, Claude) to optimize the SDLC and automate complex testing/generation tasks.
Advanced Degree: B.

This position is open to all candidates.

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8588269

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אירעה שגיאה בשליחת פרטיך למשרה

10/04/2026

Senior Data & Machine Learning Engineer

חברה חסויה

Location: Tel Aviv-Yafo

Job Type: Full Time

We are looking for a Senior Data & Machine Learning Engineer to operate at the intersection of data platform engineering and machine learning enablement. This role is responsible for building scalable, efficient, and reliable data systems while enabling Data Science and Analytics teams to develop and deploy ML-driven features.

You will take ownership of the data and ML infrastructure layer, ensuring that pipelines, storage models, and compute usage are optimized, while also shaping how data workflows and ML solutions are designed across the organization.

Responsibilities
Data Platform & Infrastructure

Design, build, and maintain scalable data pipelines and storage systems supporting analytics and ML use cases
Ensure compute and cost efficiency across pipelines, storage models, and processing workflows
Own and improve data orchestration, transformation, and serving layers (e.g., Spark, DBT, streaming/batch systems)
Build and maintain shared infrastructure components, including:
IO managers and data access abstractions
Integrations with DBT, Spark, and other data frameworks
Internal tooling to improve developer productivity and reliability
ML Enablement & Collaboration

Partner closely with Data Science to design and productions ML solutions for new features and research initiatives
Translate experimental models into robust, scalable production systems
Support feature engineering, training pipelines, and inference workflows
Help define best practices for ML lifecycle management (training, validation, deployment, monitoring)
Data Quality, Governance & Best Practices

Enforce best practices for building and maintaining data processes across Data Analyst and Data Science teams
Define standards for:
Data modeling and transformations
Pipeline reliability and observability
Testing, versioning, and documentation
Improve data quality, consistency, and discoverability across the organization
Performance & Reliability

Optimize systems for performance, scalability, and cost efficiency
Monitor and troubleshoot data pipelines and ML systems in production
Implement observability (logging, metrics, alerting) across data workflows

Requirements:
Strong programming skills in Python (or similar language)
Proven experience building and maintaining production-grade data pipelines
Hands-on experience with data processing frameworks (e.g., Spark or similar)
Familiarity with DBT or modern data transformation workflows
Experience working with cloud environments (AWS, GCP, or Azure)
Solid understanding of data modeling, distributed systems, and ETL/ELT patterns

This position is open to all candidates.

עדכון קורות החיים לפני שליחה

8604541

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אירעה שגיאה בשליחת פרטיך למשרה

05/04/2026

Machine Learning Engineer Tech Lead

חברה חסויה

Location: Tel Aviv-Yafo

Job Type: Full Time

We are looking for an ML Engineer / MLOps Tech Lead to promote machine learning engineering excellence. Someone who is passionate about building scalable, high-quality data products and processes, while ensuring production systems maintain strong real-time performance observability.
You will focus on designing and maintaining the core infrastructure that empowers the Machine Learning Engineers working within Data Science product teams. Youll collaborate closely with stakeholders across data science, product, and engineering, playing a pivotal role in driving the business by architecting and enabling the infrastructure for machine learning model development, serving, and lifecycle management-the foundation of our product.
Responsibilities:
Collaborate with product, data science, and engineering teams to solve complex problems, identify trends, and create opportunities through robust ML infrastructure.
End-to-end ML delivery - enabling model performance development, training, validation, testing, and version control.
Build and support monitoring and observability tools - dashboards, alerts, and performance tracking of models in production.
Lead architecture projects such as: Feature Store, Vector / Graph Databases.
Data wrangling - supporting and enabling data requirements for research, training, validation, and testing.
Drive engineering best practices including code and model versioning, CI/CD pipelines, rollout strategies, and disaster recovery procedures.

Requirements:
3+ years of experience as an ML Engineer / MLOps
5+ years of experience as a software engineer or data engineer
2+ years of experience in a technical leadership role (leading engineers or data scientists)
Strong programming skills in Python and SQL
Hands-on experience with MPP frameworks such as Spark, Flink, Ray, Dask or equivalent
Strong analytical and critical thinking skills

This position is open to all candidates.

עדכון קורות החיים לפני שליחה

8600846

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