Founding ML Engineer

3 ימים

Senior Machine Learning Engineer, MLOps

חברה חסויה

Location: Tel Aviv-Yafo

Job Type: Full Time

Were seeking a Senior Machine Learning Engineer with strong MLOps abilities to own the production ML systems behind user engagement, cardiovascular risk stratification, and personalized health recommendations in the app. You'll build and run the CI/CD, real-time serving, versioning, and monitoring infrastructure that models depend on, working closely with data scientists who own the modeling to make sure what they build runs reliably at scale. You should have a proven track record of shipping ML systems to production and be comfortable using AI coding assistants as a core part of your workflow.
Responsibilities:
Build and own production ML infrastructure, including CI/CD pipelines, real-time model serving, model versioning and registries, automated evaluation pipelines, monitoring, and observability.
Serve models in real time at scale, with a strong focus on low-latency inference, reliability, and graceful degradation under load, and operate batch inference with clear standards for latency, availability, correctness, and cost.
Write high-quality, maintainable, well-tested production-grade code, and own its observability, debugging, reliability, and scalability in production.
Implement model monitoring for drift, data quality, latency, and performance degradation, with automated retraining.

Requirements:
4+ years of software engineering experience, including 2+ years of hands-on MLOps, building and owning production ML infrastructure on AWS or GCP with a data or ML platform (e.g., Snowflake ML, Databricks, SageMaker, Vertex AI, JFrog ML).
Strong Python skills, with production-grade, maintainable, well-tested code.
Hands-on experience building CI/CD pipelines for ML: automated testing, training, validation, and deployment of models (e.g., Cloud Build, Cloud Deploy, Jenkins, GitHub Actions).
Experience serving models in real time at scale: low-latency inference, autoscaling, and reliable serving architectures.
Experience owning a solution end-to-end, from pipeline and model integration through deployment and production debugging.
Proficiency using AI coding assistants as a core part of the development workflow.
Nice-to-Haves:
Hands-on model development experience: feature engineering, training, and evaluation, with frameworks such as PyTorch, scikit-learn, XGBoost, or LightGBM (plus YOLO/OCR, supervised/unsupervised learning).
Hands-on experience integrating LLMs into production systems (e.g., LangChain, LangGraph, AWS Bedrock, RAG).
Familiarity with ML lifecycle tooling: experiment tracking, model registries, feature stores (MLflow, DVC, Feast, Weights & Biases).
Experience with workflow orchestration tools (Airflow, Dagster, Prefect, Kubeflow Pipelines).
Experience with monitoring/observability stacks (Prometheus, Grafana, Datadog).

This position is open to all candidates.

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8855646

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30/09/2026

Backend Engineer - ML Platform

חברה חסויה

Location: Tel Aviv-Yafo

Job Type: Full Time

We're looking for a Backend Engineer to help build and scale the Machine Learning Platform that powers how we use AI across the business. You'll be part of the ML Platform team, designing the infrastructure that lets our data scientists move faster, ship smarter, and operate with confidence in production.
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 you'll:
Design and build the foundational ML platform and AI agents to accelerate data science model delivery across all business units
Architect cloud-native microservices running on Kubernetes, using infrastructure-as-code to automate model deployment and management
Own the end-to-end ML lifecycle, covering training, testing, deployment, and real-time monitoring
Evaluate and choose the right tools and technologies based on workload demands and performance requirements
Collaborate with engineering, data science, and product teams to keep ML projects aligned with business goals
Identify and fix reliability, scalability, and performance gaps before they become problems.

Requirements:
What you'll need
3+ years of software engineering experience, with a strong record of delivering high-scale, production-grade systems
Strong proficiency in Python
Hands-on experience with relational and NoSQL databases, and at least one major cloud platform (AWS, Azure, or GCP)
Experience with training, testing, deploying, and monitoring real-time or near real-time ML models in production
Fluent with AI-powered development tools like Cursor and Claude Code, and genuinely curious about what's next in GenAI, LLMs, and AI agents
Familiarity with AI concepts like RAG, embeddings, mixture-of-experts, prompt crafting, and LLM context engineering - an advantage
Sharp problem-solving instincts and the ability to move fast without cutting corners
Bachelor's or Master's degree in Computer Science, Engineering, Statistics, or a related field
Ready to work in an office environment most days of the week.

This position is open to all candidates.

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8837916

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27/09/2026

Principal/Senior ML Platform Engineer (Cortex)

חברה חסויה

Location: Tel Aviv-Yafo

Job Type: Full Time

We are looking for a Principal MLOps Engineer with a deep focus on ML Platforms and Infrastructure to join our Data & AI group at Cortex Research. Our team is responsible for designing, building, and scaling the foundational MLOps and LLMOps platforms that power both our Data Scientists and Security Researchers. You will architect the high-performance core infrastructure that enables these roles to build, train, and deploy advanced AI systems-ranging from optimized Small Language Models (SLMs) to complex agentic workflows and RAG systems. If you are passionate about building scalable compute platforms and automating the full ML lifecycle to solve complex data and security challenges, we want to hear from you.
Key Responsibilities
Scale Distributed Training: Design and optimize infrastructure for training and fine-tuning LLMs and SLMs, leveraging distributed GPU workloads, efficient clustering, and compute optimization.
Automate the ML Lifecycle: Architect robust, automated pipelines for continuous training (CT) and deployment (CD) of models, ensuring a seamless flow from raw data collection to production environments.
Build Model Infrastructure: Own the serving architecture for LLMs/SLMs, balancing latency, throughput, and GPU utilization under production traffic.
Implement Advanced Monitoring: Establish comprehensive observability systems to monitor live model performance, data drift, and computational metrics, feeding insights back into the automated training loops for continuous improvement.
Collaborative Architecture: Partner closely with data scientists and security researchers to productize complex model architectures and streamline their workflows, while collaborating with our DevOps team to integrate with core cloud infrastructure.

Requirements:
Required Qualifications
Core Engineering: 4+ years experience as a Senior ML Engineer, MLOps Engineer, or Backend Platform Engineer (Hands-On) working with cloud environments.
Model Lifecycle Engineering: Hands-on experience managing the technical lifecycle of diverse model architectures, spanning classic ML, LLMs/SLMs, and agentic/RAG systems. This includes engineering scalable data preparation and processing pipelines as well as implementing infrastructure for model training, fine-tuning, optimization, and high-throughput production serving.
Distributed Training & Compute: Strong foundational knowledge of Deep Learning concepts (neural network architectures, training dynamics, optimization techniques) paired with proven experience setting up and optimizing distributed training workloads across multiple GPUs (using PyTorch, DeepSpeed, Megatron-LM, or cloud-native training infrastructure).
Cloud & Infrastructure Architecture: Strong infrastructure knowledge within a major cloud provider ecosystem (GCP, AWS, or Azure), specifically leveraging managed AI platforms and services.
Python Expertise: Expert-level Python skills focused on ML infrastructure, pipelines, and automation frameworks.
CI/CD Integration: Experience with modern CI/CD patterns (such as GitLab CI or GitHub Actions) for automating software and model delivery loops.
AI Tooling & Development: Proficient in leveraging day-to-day AI tools and ecosystems (e.g., Claude, Gemini, MCPs, custom skills, and markdown formatting) to generate, review, and test code dynamically within your development cycle.
Preferred Qualifications:
Strong GCP ecosystem experience.
Background in data science or deep learning workflows.
Cybersecurity domain knowledge.

This position is open to all candidates.

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8834018

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27/09/2026

Principal/Senior ML Platform Engineer (Cortex)

חברה חסויה

Location: Tel Aviv-Yafo

Job Type: Full Time

We are looking for a Principal MLOps Engineer with a deep focus on ML Platforms and Infrastructure to join our Data & AI group at Cortex Research. Our team is responsible for designing, building, and scaling the foundational MLOps and LLMOps platforms that power both our Data Scientists and Security Researchers. You will architect the high-performance core infrastructure that enables these roles to build, train, and deploy advanced AI systems-ranging from optimized Small Language Models (SLMs) to complex agentic workflows and RAG systems. If you are passionate about building scalable compute platforms and automating the full ML lifecycle to solve complex data and security challenges, we want to hear from you.
Key Responsibilities
Scale Distributed Training: Design and optimize infrastructure for training and fine-tuning LLMs and SLMs, leveraging distributed GPU workloads, efficient clustering, and compute optimization.
Automate the ML Lifecycle: Architect robust, automated pipelines for continuous training (CT) and deployment (CD) of models, ensuring a seamless flow from raw data collection to production environments.
Build Model Infrastructure: Own the serving architecture for LLMs/SLMs, balancing latency, throughput, and GPU utilization under production traffic.
Implement Advanced Monitoring: Establish comprehensive observability systems to monitor live model performance, data drift, and computational metrics, feeding insights back into the automated training loops for continuous improvement.
Collaborative Architecture: Partner closely with data scientists and security researchers to productize complex model architectures and streamline their workflows, while collaborating with our DevOps team to integrate with core cloud infrastructure.

Requirements:
Required Qualifications
Core Engineering: 4+ years experience as a Senior ML Engineer, MLOps Engineer, or Backend Platform Engineer (Hands-On) working with cloud environments.
Model Lifecycle Engineering: Hands-on experience managing the technical lifecycle of diverse model architectures, spanning classic ML, LLMs/SLMs, and agentic/RAG systems. This includes engineering scalable data preparation and processing pipelines as well as implementing infrastructure for model training, fine-tuning, optimization, and high-throughput production serving.
Distributed Training & Compute: Strong foundational knowledge of Deep Learning concepts (neural network architectures, training dynamics, optimization techniques) paired with proven experience setting up and optimizing distributed training workloads across multiple GPUs (using PyTorch, DeepSpeed, Megatron-LM, or cloud-native training infrastructure).
Cloud & Infrastructure Architecture: Strong infrastructure knowledge within a major cloud provider ecosystem (GCP, AWS, or Azure), specifically leveraging managed AI platforms and services.
Python Expertise: Expert-level Python skills focused on ML infrastructure, pipelines, and automation frameworks.
CI/CD Integration: Experience with modern CI/CD patterns (such as GitLab CI or GitHub Actions) for automating software and model delivery loops.
AI Tooling & Development: Proficient in leveraging day-to-day AI tools and ecosystems (e.g., Claude, Gemini, MCPs, custom skills, and markdown formatting) to generate, review, and test code dynamically within your development cycle.
Preferred Qualifications:
Strong GCP ecosystem experience.
Background in data science or deep learning workflows.
Cybersecurity domain knowledge.

This position is open to all candidates.

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8834083

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27/09/2026

Principal/Senior ML Platform Engineer (Cortex)

חברה חסויה

Location: Tel Aviv-Yafo

Job Type: Full Time

We are looking for a Principal MLOps Engineer with a deep focus on ML Platforms and Infrastructure to join our Data & AI group at Cortex Research. Our team is responsible for designing, building, and scaling the foundational MLOps and LLMOps platforms that power both our Data Scientists and Security Researchers. You will architect the high-performance core infrastructure that enables these roles to build, train, and deploy advanced AI systems-ranging from optimized Small Language Models (SLMs) to complex agentic workflows and RAG systems. If you are passionate about building scalable compute platforms and automating the full ML lifecycle to solve complex data and security challenges, we want to hear from you.
Key Responsibilities
Scale Distributed Training: Design and optimize infrastructure for training and fine-tuning LLMs and SLMs, leveraging distributed GPU workloads, efficient clustering, and compute optimization.
Automate the ML Lifecycle: Architect robust, automated pipelines for continuous training (CT) and deployment (CD) of models, ensuring a seamless flow from raw data collection to production environments.
Build Model Infrastructure: Own the serving architecture for LLMs/SLMs, balancing latency, throughput, and GPU utilization under production traffic.
Implement Advanced Monitoring: Establish comprehensive observability systems to monitor live model performance, data drift, and computational metrics, feeding insights back into the automated training loops for continuous improvement.
Collaborative Architecture: Partner closely with data scientists and security researchers to productize complex model architectures and streamline their workflows, while collaborating with our DevOps team to integrate with core cloud infrastructure.

Requirements:
Required Qualifications
Core Engineering: 4+ years experience as a Senior ML Engineer, MLOps Engineer, or Backend Platform Engineer (Hands-On) working with cloud environments.
Model Lifecycle Engineering: Hands-on experience managing the technical lifecycle of diverse model architectures, spanning classic ML, LLMs/SLMs, and agentic/RAG systems. This includes engineering scalable data preparation and processing pipelines as well as implementing infrastructure for model training, fine-tuning, optimization, and high-throughput production serving.
Distributed Training & Compute: Strong foundational knowledge of Deep Learning concepts (neural network architectures, training dynamics, optimization techniques) paired with proven experience setting up and optimizing distributed training workloads across multiple GPUs (using PyTorch, DeepSpeed, Megatron-LM, or cloud-native training infrastructure).
Cloud & Infrastructure Architecture: Strong infrastructure knowledge within a major cloud provider ecosystem (GCP, AWS, or Azure), specifically leveraging managed AI platforms and services.
Python Expertise: Expert-level Python skills focused on ML infrastructure, pipelines, and automation frameworks.
CI/CD Integration: Experience with modern CI/CD patterns (such as GitLab CI or GitHub Actions) for automating software and model delivery loops.
AI Tooling & Development: Proficient in leveraging day-to-day AI tools and ecosystems (e.g., Claude, Gemini, MCPs, custom skills, and markdown formatting) to generate, review, and test code dynamically within your development cycle.
Preferred Qualifications
Strong GCP ecosystem experience.
Background in data science or deep learning workflows.
Cybersecurity domain knowledge.

This position is open to all candidates.

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8834186

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10/09/2026

ML Software Engineer

חברה חסויה

Location: Tel Aviv-Yafo

Job Type: Full Time

We are looking for a strong Backend Software Engineer to bridge the gap between our Machine Learning research team and our enterprise production systems. You will act as the technical backbone for our ML Scientists - by advising, designing and implementing the production facing features. If you are a backend expert who wants to solve complex system architecture challenges and dive into the world of ML platforms & Agentic LLM pipelines, this is the role for you - An exciting role collaborating with ML science team, data/infra team and DevOps to drive real customer impact.

As a ML Engineer, you will:

Lead ML delivery: transforming research output (code, models, ideas) into robust, scalable, low-latency microservices in production

Help architect e2e solutions to real customer pains ranging from ingestion, integration, ETLs, DB design up to low-latency services

Design, build, and maintain automated workflows for ML models, including auto-trains, benchmarking, testing, performance gating, and production deployment.

Tackle complex backend challenges: optimizing API response times, managing database connectivity and concurrency at scale, balancing accuracys drive for complex questions with the business needs of fast responsiveness by making hard technical trade-offs between customer gains and business costs.

Design and optimize data pipelines and ETL processes, connecting our Snowflake data warehouse to our training environments.

Work within our existing ML infrastructure (Kubeflow, MLflow, KServe) to ensure smooth model lifecycles and performance monitoring.

Collaborate closely with ML Scientists, guiding them on software engineering best practices without slowing down their research.

Monitor and optimize production models for performance, cost efficiency, availability, and observability.

Requirements:
6+ years of backend software engineering experience designing, building, and maintaining large-scale, high-throughput production systems

Strong coding skills, Ability to write clean, maintainable code, OOP familiarity, package design, microservices etc.
Note: Work is in python, but strong engineers with deep Java/C# backgrounds who have some Python experience and are willing to transition fully are highly encouraged to apply.

Solid Database design & SQL skills, Deep understanding of SQL, experience working with relational and/or bigdata (columnar) databases, ORMs, and efficient query design.

API & Performant Design Proven experience - building robust systems, you understand how to handle concurrency, ETL tradeoffs, building fault-tolerant best effort data flows

This position is open to all candidates.

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8818291

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5 ימים

Senior Product Manager

חברה חסויה

Location: Tel Aviv-Yafo

Job Type: Full Time

As a Senior Product Manager , you will sit at the intersection of data science, engineering, and product, translating complex ML and causal inference capabilities into a product our customers love. You will work hand-in-hand with our data science and the engineering teams to shape how models are designed, evaluated, and delivered into production.

Own a significant part of the product roadmap for our value measurement platform, with a focus on data science driven features (causal inference, incrementality modeling, attribution, and media mix optimization) and customer onboarding.
Partner closely with data scientists from problem framing through experimentation, model validation, and productization, youll be expected to critically read model specs, challenge assumptions, and help prioritize research directions based on customer impact
Define success metrics and evaluation frameworks for ML models alongside the DS team, balancing statistical rigor with business usability
Own the UX of data-heavy features: collaborate with designers and data scientists to turn complex model outputs into intuitive user flows, clear visualizations, and dashboards that customers can actually act on
Design and prioritize dashboards, reports, and in-product analytics, defining what metrics to surface, how to visualize uncertainty and causal results, and how to make insights discoverable without overwhelming the user
Run user research and customer interviews to validate both the analytical value and the usability of features, and onboarding integration; translate findings into concrete design and product improvements
Translate customer problems into well-scoped technical and design requirements; translate model outputs and methodology into clear value propositions for customers
Prepare the implementation of features and support engineering, design, and DS teams through design reviews, QA, and launch, including writing acceptance criteria that account for model behavior, edge cases, data quality, and UX states (empty, loading, partial data, error)
Ensure data integrity through testing, acceptance tests, and ongoing monitoring across our diverse range of data products
Work with clients and internal stakeholders to understand business goals, identify relevant KPIs, and feed those insights back into the product, modeling, and dashboarding roadmap
Oversee end-to-end data flow and engineering processes, collaborating with ML, data, and engineering teams to ensure efficient and accurate operation during critical phases like client onboarding and daily production runs
Strategically plan for missing or imperfect data, designing product behavior and UX for new client onboarding, process failures, client-specific configurations, long-term seasonality, and external signals/features
Drive experimentation: help design A/B tests, back-tests, and validation studies to assess the impact of new models, features, and UX changes

דרישות:
Min. 4 years of work experience in a product management role, experience in SaaS, B2B and/or data-heavy / ML-driven products is strongly preferred
Proven track record of shipping products that involve machine learning, statistical modeling, or data pipelines, comfortable being the PM counterpart to a data science team
Technical fluency: able to read and discuss ML/statistical concepts (regression, causal inference, Bayesian methods, time series) with data scientists, and able to reason about trade-offs in model design
Hands-on comfort with SQL and the ability to independently query, explore, and validate data; familiarity with Python (notebooks, basic scripting) is a strong plus
Understanding of data infrastructure concepts, ETL/ELT pipelines, data quality, distributed processing (Spark, Kubernetes, Argo, or similar), enough to collaborate meaningfully with engineering
Strong UX sensibility for data products, experience partnering with designers to turn complex data and model outputs into clear, usable interfaces; comfo המשרה מיועדת לנשים ולגברים כאחד.

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8851452

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04/10/2026

Senior ML Research Engineer 25769

חברה חסויה

Location: Tel Aviv-Yafo

Job Type: Full Time

Join our companys AI research group, a cross-functional team of ML engineers, researchers and security experts building the next generation of AI-powered security capabilities. Our mission is to leverage large language models to understand code, configuration, and human language at scale, and to turn this understanding into security AI capabilities which will drive our company AI future security solutions.
We foster a hands-on, research-driven culture where youll work with large-scale data, modern ML infrastructure, and a global product footprint that impacts over 100,000 organizations worldwide.
Key Responsibilities
Your Impact & Responsibilities
As a Senior ML Research Engineer, you will be responsible for the end-to-end lifecycle of large language models: from data definition and curation, through training and evaluation, to providing robust models that can be consumed by product and platform teams.
Own training and fine-tuning of LLMs / seq2seq models: Design and execute training pipelines for transformer-based models (encoder-decoder, decoder-only, retrievalaugmented, etc.), and fine-tune open-source LLMs on our company-specific data (security content, logs, incidents, customer interactions).
Apply advanced LLM training techniques such as instruction tuning, preference / contrastive learning, LoRA / PEFT, continual pre-training, and domain adaptation where appropriate.
Work deeply with data: define data strategies with product, research and domain experts; build and maintain data pipelines for collecting, cleaning, de-duplicating and labeling large-scale text, code and semi-structured data; and design synthetic data generation and augmentation pipelines.
Build robust evaluation and experimentation frameworks: define offline metrics for LLM quality (task-specific accuracy, calibration, hallucination rate, safety, latency and cost); implement automated evaluation suites (benchmarks, regression tests, redteaming scenarios); and track model performance over time.
Scale training and inference: use distributed training frameworks (e.g. DeepSpeed, FSDP, tensor/pipeline parallelism) to efficiently train models on multi-GPU / multi-node clusters, and optimize inference performance and cost with techniques such as quantization, distillation and caching.
Collaborate closely with security researchers and data engineers to turn domain knowledge and threat intelligence into high-value training and evaluation data, and to expose your models through well-defined interfaces to downstream product and platform teams.

Requirements:
What You Bring
5+ years of hands-on work in machine learning / deep learning, including 3+ years focused on NLP / language models.
Proven track record of training and fine-tuning transformer-based models (BERT-style, encoder-decoder, or LLMs), not just consuming hosted APIs.
Strong programming skills in Python and at least one major deep learning framework (PyTorch preferred; TensorFlow).
Solid understanding of transformer architectures, attention mechanisms, tokenization, positional encodings, and modern training techniques.
Experience building data pipelines and tools for large-scale text / log / code processing (e.g. Spark, Beam, Dask, or equivalent frameworks).
Practical experience with ML infrastructure, such as experiment tracking (Weights & Biases, MLflow or similar), job orchestration (Airflow, Argo, Kubeflow, SageMaker, etc.), and distributed training on multi-GPU systems.
Strong software engineering practices: version control, code review, testing, CI/CD, and documentation.
Ability to own research and engineering projects end-to-end: from idea, through prototype and controlled experiments, to models ready for integration by product and platform teams.
Good communication skills and the ability to work closely with non-ML stakeholders (security experts, product managers, engineers).

This position is open to all candidates.

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8840766

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22/09/2026

Data Scientist

חברה חסויה

Location: Tel Aviv-Yafo

Job Type: Full Time

The ideal candidate is passionate about data and AI, comfortable navigating complex systems, and excited by the opportunity to operationalize AI within a modern enterprise environment. We value curiosity as much as experience: we are looking for someone eager to show what they know, and equally eager to keep learning in a field that moves fast.

Responsibilities
Explore, analyze, and model large volumes of structured and unstructured data in Python, from exploratory analysis and feature engineering through to model validation and communication of results.
Design, train, evaluate, and deploy machine learning models, and monitor their performance, accuracy, and drift in production.
Build and orchestrate Agentic AI solutions - LLM-based agents, RAG pipelines, prompt design, and evaluation frameworks - to automate data quality checks, investigation, and reporting workflows.
Integrate models and agents with internal systems and data sources using MCP servers and clients, and workflow automation platforms such as n8n.
Write efficient and maintainable SQL queries to support analysis, reporting, and data exploration needs.
Collaborate with data engineers to productionise models and agents: reliable data flows, logging, alerting, and performance tuning.
Participate in the development of internal tools and dashboards that make data and AI capabilities accessible across the organization.
Share findings with the team and help evaluate emerging AI tooling as the ecosystem evolves.

Requirements:
Knowledge and Experience
3+ years of experience as a Data Scientist, ML Engineer, or in a similar analytical role.
Strong programming skills in Python, with experience writing reusable libraries and working with data manipulation and ML libraries (e.g., pandas, NumPy, scikit-learn, PyTorch/TensorFlow).
Solid grounding in statistics and machine learning: feature engineering, model selection, validation, and interpreting results for a business audience.
Hands-on experience with LLMs and Agentic AI: prompt engineering, retrieval-augmented generation (RAG), tool/function calling, and building or consuming agent frameworks.
Advanced proficiency in SQL and experience working with large-scale databases (e.g., PostgreSQL, MSSQL, Oracle).
Experience with AI/ML workflows, supporting model training, inference, and evaluation pipelines in production environments.
Genuine curiosity and a strong appetite to learn - eager to bring existing knowledge to the team and to grow it further.

Preferred Knowledge and Experience
Experience building or consuming MCP (Model Context Protocol) servers and clients.
Experience with workflow automation / orchestration platforms such as n8n, Airflow, or similar.
Experience with data visualisation and BI tooling for communicating analytical results.
Exposure to real-time data processing technologies (e.g., Kafka, Spark Streaming).
Background in finance, trading systems, or financial market data.

This position is open to all candidates.

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8827691

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המשרה נמחקה

תוכל לצפות בה בדף המשרות שלי

המשרה הוחזרה לרשימת תוצאות החיפוש

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אירעה שגיאה בשליחת פרטיך למשרה

04/10/2026

Senior AI/ML Engineer

חברה חסויה

Location: Tel Aviv-Yafo

Job Type: Full Time

A Senior AI/ML Engineer role on our Data team, sitting at the intersection of AI engineering, data science, and data platform. You'll build agentic, LLM-based systems that turn vast amounts of unstructured information into high-quality structured signals - powering our products and surfacing legal risk - owning each solution across the full lifecycle, from exploration to production. You'll work closely with data engineers, data scientists, product managers, and legal experts to turn ambiguous problems into scalable AI capabilities. This is a hands-on role for someone with strong engineering judgment, deep data/ML expertise, and a focus on reliability beyond the prototype stage.

What You'll Do

Design, build, deploy, and operate production AI systems: agentic, LLM-based extraction workflows and batch/online inference pipelines and owning them from exploration through deployment, monitoring, and iteration.
Define quality metrics and build evaluation datasets, testing processes, and feedback loops for ML and LLM systems; monitor accuracy, latency, cost, and data quality, and investigate and fix failures.
Explore large, complex datasets to surface useful features and patterns, and translate findings into production-grade implementations.
Partner with data engineers on ingestion, transformation, orchestration, and storage to deliver dependable data products.
Use AI-assisted coding tools (e.g., Claude) effectively while maintaining strong technical judgment and ownership of your systems.
Help shape engineering standards for production AI: testing, observability, reproducibility, data lineage, versioning, and safe deployment.

Requirements:
6+ years in ML engineering, AI engineering, data science, or backend engineering for data-intensive systems, with hands-on experience taking systems to production (deployment, monitoring, evaluation, versioning, improvement).
Strong Python skills and data orientation - SQL, NoSQL, data pipelines, and both structured and unstructured data.
Solid data science foundation: feature engineering, statistical reasoning, experiment design, error analysis, and relevant metrics (precision, recall, accuracy, coverage).
Experience with LLM-based applications: structured extraction, tool calling, agentic workflows, prompt design, embeddings, retrieval, and model evaluation.
Understanding of production AI tradeoffs, quality, latency, scalability, reliability, cost and experience integrating AI into batch, orchestration, API, or event-driven workflows.
Ability to work independently in ambiguous environments, breaking problems down and driving them to production.
Strong communication skills across engineering, data science, product, and domain-expert teams.
Nice to Have

Document intelligence, information extraction, entity resolution, classification, ranking, or large-scale enrichment pipelines.
Experience building or operating agentic systems in production.
Natural Language Processing experience.
Familiarity with orchestration/ML tooling (Airflow, Dagster, Prefect, MLflow, or similar).
Experience with data warehouses, data lakes, vector databases, or modern data-processing frameworks.
Cloud-native experience: AWS, Docker, Kubernetes, IaC, CI/CD.
Experience with human-in-the-loop review or feedback-driven AI systems.
Familiarity with legal, financial, compliance, or other data-intensive domains where quality and explainability matter.

This position is open to all candidates.

עדכון קורות החיים לפני שליחה

8841795

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