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We are seeking a talented and self-driven experienced Data Scientist to help advance our machine learning capabilities.This is a key role for someone passionate about leveraging machine learning to solve complex, real-world problems and deliver measurable business impact.

Responsibilities:

Develop and implement state-of-the-art econometric and machine learning models for demand forecasting.
Conduct research and experimentation to evaluate novel approaches for improving accuracy, robustness, and scalability.
Collaborate with cross-functional teams (including product, data engineering, MLOPS and Platform) to deploy ML systems in production.
Clearly communicate complex technical findings to non-technical stakeholders, including product leaders and executives.
Requirements

We are seeking an MLOps Engineer to help us grow our technical team's capabilities. The ideal candidate has relevant experience in data engineering, preferably within the AI field. Aviation industry experience would be a great addition.

You will be responsible for building and maintaining models and data pipelines that power our data science workflows. You'll play a crucial role in ensuring the accuracy, consistency, and efficiency of the data we use for model training and inference. This involves working with both structured and unstructured data from various sources, leveraging your expertise in data engineering and machine learning to create a robust and scalable system.

We are seeking a detail-oriented and collaborative Senior ML Engineer to help support and maintain our machine learning capabilities. This role is ideal for someone who enjoys working closely with production systems, ensuring reliability, scalability, and explainability of models while enabling research teams to deliver impact faster.



Responsibilities



Collaborate with cross-functional teams to ensure ML systems remain robust, explainable, and aligned with business needs.
Monitor and report on ML model performance, reliability, and explainability metrics.
Participate in model retraining procedures, implement automation and optimization of MLOps pipelines.
Extend and scale monitoring pipelines, including support for new features in development.
Investigate, troubleshoot, and resolve issues in production ML workflows (tiered support from initial triage to root-cause analysis with model owners).
Develop and maintain repositories for feature engineering, inference monitoring pipelines, and artifact monitoring tools.
Perform exploratory data analysis (EDA) on historical datasets to identify quality issues and maintain data health.
Implement and oversee production based adjusters across customer deployments.
Evaluate and track critical ML artifacts such as explainability files, coverage metrics, and alignment of features.
Support development and maintenance of internal tools (e.g., interfaces, registries, and feature monitoring frameworks).
Build and maintain static and temporal features, including seasonality, event-based, and price-related features.

We are looking for an MLOps Team Leadto drive the development of an internal machine learning platform for a group of ML teams. This is a hands-on leadership role: you will guide a small team of MLOps engineers that builds the automation and infrastructure powering our research (R&D) workflows and runs our pipelines to production. You will take ownership of end-to-end initiatives and drive the team toward critical infrastructure and model-lifecycle milestones, while staying close enough to the code to set technical direction and raise the bar by example.

You will be responsible for building and maintaining the models and data pipelines behind our data science workflows, ensuring the accuracy, consistency, and efficiency of the data used for training and inference, working across structured and unstructured data from many sources on a large-scale, distributed platform.

WHAT YOU WILL DO

- Lead, mentor, and grow a team of MLOps engineers, owning delivery and technical quality.

- Take end-to-end ownership of infrastructure and pipeline initiatives across the LMM group, from design through production.

- Stay hands-on: contribute to design and code, review work, and set engineering standards.

- Drive the team through critical milestones in ML model-lifecycle and infrastructure ownership.

- Partner with R&D and other stakeholders to translate research needs into robust, scalable systems.

- Help evolve the platform, including our ongoing migration from Dask to Ray.

We are seeking an MLOps Engineer to help us grow our technical team's capabilities. The ideal candidate has relevant experience in data engineering, preferably within the AI field. Aviation industry experience would be a great addition.

You will be responsible for building and maintaining models and data pipelines that power our data science workflows. You'll play a crucial role in ensuring the accuracy, consistency, and efficiency of the data we use for model training and inference. This involves working with both structured and unstructured data from various sources, leveraging your expertise in data engineering and machine learning to create a robust and scalable system.

We are seeking a detail-oriented and collaborative Senior ML Engineer to help support and maintain our machine learning capabilities. This role is ideal for someone who enjoys working closely with production systems, ensuring reliability, scalability, and explainability of models while enabling research teams to deliver impact faster.



Responsibilities



Collaborate with cross-functional teams to ensure ML systems remain robust, explainable, and aligned with business needs.
Monitor and report on ML model performance, reliability, and explainability metrics.
Participate in model retraining procedures, implement automation and optimization of MLOps pipelines.
Extend and scale monitoring pipelines, including support for new features in development.
Investigate, troubleshoot, and resolve issues in production ML workflows (tiered support from initial triage to root-cause analysis with model owners).
Develop and maintain repositories for feature engineering, inference monitoring pipelines, and artifact monitoring tools.
Perform exploratory data analysis (EDA) on historical datasets to identify quality issues and maintain data health.
Implement and oversee production based adjusters across customer deployments.
Evaluate and track critical ML artifacts such as explainability files, coverage metrics, and alignment of features.
Support development and maintenance of internal tools (e.g., interfaces, registries, and feature monitoring frameworks).
Build and maintain static and temporal features, including seasonality, event-based, and price-related features.

Self-service enablement: Build secure, automated pathways that accelerate development while keeping organizational standards intact.
Architectural modularization: Design Kubernetes-native abstractions - controllers and operators - and decouple infrastructure into independent service lifecycles that support rapid iteration.
Everything as code: Treat infrastructure, policy, and configuration with the same SDLC as application software - versioned, reviewed, tested, and CI-gated.
Proactive reliability: Own production through robust observability, clear SLOs, and high operational hygiene; define alerting that fires correctly and lead blameless postmortems that fix the system.

We are looking for a Software Engineer to join our Core Team. This team owns and evolves our core query and indexing infrastructure, working directly on our query engine, query optimizations, indexing engines and pipeline. Youll solve deep technical problems and help shape the architecture of high-performance, complex systems.





WHAT YOU WILL DO

Design, develop, and maintain connectors to Databases and SIEMs.
Add new indexing capabilities to our indexing pipeline.
Own technical decisions end-to-end from design to production rollout.
Collaborate closely with product, infra, and data teams to align on technical direction and integration.

We're looking for a Software Engineer to join our R&D team. In this role, you'll design and build scalable, high-performance systems that bring together rich backend capabilities and intuitive frontend experiences. You'll be part of a mission-driven team, building innovative technologies that enable security teams to detect, analyze, and respond to threats faster than ever before.





WHAT YOU WILL DO

Develop and own end-to-end features across the entire stack, from backend services to frontend interfaces.
Design and implement distributed backend systems that handle massive volumes of security data in real-time.
Build modern, responsive, and accessible user interfaces using React, TypeScript, Remix, Tailwind, and Shadcn.
Architect and maintain robust APIs and data contracts between the frontend and backend.
Collaborate with product, design, and security experts to deliver impactful user facing features.
Ensure high code quality by writing comprehensive unit, integration, and E2E tests using tools like Vitest, Playwright, and React Testing Library, as well as backend testing tools
Optimize system performance, reliability, and scalability on both ends of the stack.
Take part in critical architectural discussions and influence the technical direction of the platform.

As a Senior Backend Engineer , you will play a critical role in building our Data & AI Lifecycle Security Platform. You will be a core developer, responsible for navigating our product and technical architecture, working with cutting-edge technologies like Go, Kubernetes,Temporal, and with different AI platforms. In our fast-moving startup environment, we value precision and focus on business goals, ensuring that every development decision is aligned with delivering impactful results quickly.



What Youll Do

Design, build, and operate scalable backend services using Go, Kubernetes, and Temporal
Collaborate with Product, Research, and Platform teams to translate business needs into technical solutions
Integrate with AI and LLM platforms, cloud providers, and data sources
Own architectural decisions while balancing speed, reliability, and long-term maintainability
Implement best practices for code quality, testing, observability, and production readiness
Debug and resolve complex production issues and improve system resilience

As a Senior DevOps Engineer , youll play a critical role in scaling and evolving our infrastructure as we grow. Youll work alongside experienced engineers to drive automation, optimize cloud operations, and ensure our systems are secure, resilient, and high-performing. This role is perfect for a hands-on engineer who thrives in fast-paced environments and wants to shape infrastructure practices at a product-focused security startup.



What Youll Do

Own and Evolve Infrastructure: Design, deploy, and operate infrastructure on AWS using Kubernetes to orchestrate containerized services.
Build Tools and Automate Everything: Streamline internal workflows with smart tooling, configuration management, and monitoring systems.
Lead CI/CD Improvements: Define and refine deployment pipelines to support rapid, reliable releases and cross-team agility.
Strengthen Edge Security: Manage WAFs, gateways, and load balancers to balance strong protection with great user experience.
Drive Reliability: Lead incident detection, troubleshooting, and automated recovery-improving uptime and system robustness continuously.

A leading provider of SaaS-based software solutions for comprehensive Human Capital Management, is seeking a Mainframe Developer to join a highly professional and experienced development team.
About the Role:
Join the development department responsible for the company's Time & Attendance platform, working on a complex data processing system developed in PL/I within a Mainframe environment.
In this role, you will contribute to the development and maintenance of mission-critical core systems, taking part in both technical design and implementation.
Key Responsibilities:
Develop and maintain core systems in a VM/VSE Mainframe environment.
Analyze requirements and develop solutions for critical business systems.
Write technical and functional design documentation.
Troubleshoot and resolve complex technical challenges with innovative solutions.
Work closely with internal teams to support and improve core platform capabilities.

Were looking for a Senior MLOps Engineer to be a core driver in how our product empowers security teams. You will be expected to deeply understand customer needs and translate them directly into product features that deliver real value. You'll own key parts of our frontend stack, drive key architectural decisions, and turn complex security data into clear, actionable business insights.

As we scale our AIDR product and expand deeper into model-driven security intelligence, we are looking for a Senior MLOps Engineer to own the infrastructure, tooling, and operational foundations that power our NLP and LLM training, evaluation, and deployment workflows.

You will architect and operate the systems that enable us to train, fine-tune, deploy, and monitor models at scale making ML reliable, fast, cost-efficient, and production-ready.

This is a high-visibility, high-impact role where you will partner closely with DevOps, Backend, Data, and Product to establish world-class ML infrastructure from the ground up.

What Youll Do

Build & Scale ML Pipelines
Design, build, and maintain pipelines for training, fine-tuning, evaluating, and deploying NLP and LLM models across GPU and CPU environments.
Establish LLM-Focused CI/CD
Implement automated CI/CD workflows for ML models, including benchmarking, testing, performance gating, and production deployment.
Optimize Runtime & Inference
Select and optimize serving frameworks for low-latency, high-throughput inference, ensuring reliability and scalability.
Own ML Infrastructure
Manage training environments, experiment tracking, model registries, artifact versioning, and distributed training systems.
Operational Excellence
Monitor and optimize production models for performance, cost efficiency, availability, and observability.

A leading Israeli software company, providing advanced Web and Mobile solutions for enterprise clients, is seeking a Development Manager. The organization delivers core business systems for major enterprises, combining innovation with stability and offering significant impact on key processes.
Location & Work Model: Tel aviv, train-line, hybrid work model.
Key Responsibilities:
Define and drive the technological vision in line with the companys strategy.
Ensure quality, stability, scalability, and security across all systems.
Manage cross-organizational interfaces with Product, Infrastructure, Security, and external vendors.
Lead and develop multiple development teams, including team leads.
Promote innovation, standardization, and expansion of development services.
Oversee projects end-to-end, including planning, prioritization, monitoring timelines and resources, and ensuring goal achievement.

we are looking for a Software Engineering Manager.
As an engineering manager at Riskified, you will lead a talented group of experienced engineers in creating a mission-critical suite of services in the payment fraud domain. The team is working to enable new Riskified product offerings, working closely with Product managers, Research and Analytics teams to validate this new offering and drive a new business forward.
The team is currently focused on Adaptive Checkout, Riskified's premier product suite that operates at the E-commerce payments level to optimize the shopping checkout flow. Your goal will be to increase conversion, reduce false declines, and enhance transaction authorization rates while using adaptive features and capabilities. We are looking for a team leader with a passion for service-oriented and microservice architectures, high performance, reliability, and clean design. You should be an AI enthusiast - someone who is not afraid to embrace and integrate new tools, whether its within the product itself, throughout the development lifecycle, or to optimize management processes. You must be able to focus, support, and drive a team while consolidating requirements from all stakeholders, displaying a high interest in our business and a proactive approach to constant improvement.
Our Tech Stack: Kubernetes, Scala, Node.js, Ruby on Rails, Kafka, Avro, gRPC, AWS, Cockroach DB, Aerospike, Claude code.