Top Machine Learning Jobs in Israel

As a Research Engineer, you'll develop systems to evaluate and secure frontier AI models, create testing frameworks, and contribute to team projects and research publications.

Build and maintain benchmarking and evaluation infrastructure for foundation and multimodal models. Design abstractions/APIs, implement metrics, integrate models and baselines, support model development, debug evaluations, and ensure reproducible, versioned workflows.

Lead backend development for Sunbit's Risk Team building low-latency, high-throughput microservices that operationalize predictive ML models. Design, scale, and maintain resilient distributed systems, collaborate with ML engineers and product teams, drive technical excellence, and mentor junior engineers.

Lead and mentor a data science team, define data strategy, manage end-to-end ML projects, analyze large datasets, build algorithmic solutions, and communicate insights to senior leaders to support revenue-driving decisions.

As a Senior Data Scientist, you will architect and implement machine learning models, influence strategy, mentor others, and tackle complex challenges in a high-stakes environment.

Design, build, and operate the server side of a large-scale visual moderation platform. Own high-throughput async pipelines for images and videos, multi-tenant systems, and cost-sensitive ML workloads. Make architectural decisions, ship iteratively, and establish practices for testing, observability, reliability, and maintainability while collaborating with engineers and product stakeholders.

Design, implement, and optimize SLAM, mapping, localization, sensor fusion, and state-estimation algorithms using LiDAR, cameras, IMU, GNSS and odometry. Integrate ML and computer-vision methods, validate in simulation and real-world, and optimize for real-time embedded/edge platforms.

Own the full lifecycle of production recommendation systems: data exploration, feature engineering, modeling (deep learning), deployment, experimentation, and measuring business KPIs. Partner with engineering to scale systems and continuously improve personalization and business impact.

Develop, train, evaluate, and deploy detection models for AI security. Translate threat research into production-ready models across ML, deep learning, LLMs, and agentic systems. Build benchmarks, labeled datasets, monitor production performance, and iterate with security researchers to ensure reliability and measurable impact.

Lead direct sales to government ministries, defense and public safety agencies in Israel. Develop territory strategy, manage complex procurements and tenders, collaborate with engineering to map solutions, cultivate executive relationships, and drive end-to-end pipeline to exceed revenue targets.

Design and develop hard/soft IP in advanced process nodes, run full simulations, collaborate with senior designers, document results, and support cross-team integration and preventive-maintenance solutions.

Create and review realistic, locale-specific robotics and natural sciences tasks in Russian to benchmark AI systems. Apply scoring rubrics, evaluate AI and human responses for accuracy and regulatory alignment, and produce high-quality gold-standard solutions and expert feedback.

Develop and improve signal-processing and ML algorithms for home sleep apnea test devices, design and run feasibility experiments, and analyze clinical trial data to support product development.

Design, implement, and operate retrieval and knowledge layers for a search vertical. Build ingestion, indexing, entity resolution, structured extraction, and evaluation pipelines. Optimize hybrid (dense+sparse) retrieval, ranking, freshness, and trust metrics, and collaborate with crawling, indexing, and ML teams to improve relevance and production performance.

Design and operate ML infrastructure for training, evaluation, batch prediction, inference, and experimentation. Build developer tooling, CI/CD workflows, observability, and reliable production ML services on Kubernetes and cloud platforms. Contribute to LLM infrastructure, including model serving, gateways, SDKs, evaluation systems, and integrations. Collaborate across research, engineering, and DevOps teams to move ML solutions from experimentation into production.

Design, build, and scale a cloud-native ML platform and AI agents. Own end-to-end ML lifecycle (training, testing, deployment, monitoring), architect Kubernetes microservices with infrastructure-as-code, ensure production reliability and performance, and collaborate with engineering, data science, and product teams to align ML projects with business goals.

Develop and optimize algorithms and ML pipelines for detection, classification, segmentation, and scene understanding. Conduct research on classical and deep learning methods, build and evaluate models for complex operational sensor data, and improve model training, optimization, and evaluation.

Design and deploy advanced ML algorithms using on-chip telemetry for lifetime prediction, clustering, parametric modeling, and time-series forecasting. Lead end-to-end algorithm lifecycle from research and POC to validation and deployment, collaborate with cross-functional teams and customers, and integrate AI-enhanced tools and intelligent pipelines into production ML workflows.

As a Machine Learning Engineer, you will design and deploy ML models, collaborate with cross-functional teams, monitor performance, and utilize MLOps best practices.

Nebius seeks a Senior ML Engineer for AI R&D focusing on applied research in reinforcement learning and agent training, requiring deep expertise in machine learning and software engineering.

Senior technical leader designing, developing, and deploying production-grade AI for medical imaging. Own end-to-end model lifecycle (data ingestion, training, evaluation, deployment, monitoring). Focus on vision-language, diffusion, and multimodal models; collaborate with scientists, engineers, product, and clinical experts to productionize AI solutions.

Build and scale an ML platform: design cloud-native microservices on Kubernetes, automate deployments with infrastructure-as-code, manage the end-to-end ML lifecycle, select tools, and ensure production reliability, scalability, and monitoring while collaborating with data science and product teams.

Process and validate complex datasets, perform quantitative analysis and data mining, develop tools to ensure data and algorithm quality, evaluate and improve algorithms produced by data scientists, and partner with Big Data Engineers to scale solutions and create new features.

The AI/ML Engineer will design and implement LLM-powered systems, integrate APIs, optimize RAG pipelines, and work collaboratively to extract insights from data.

Lead end-to-end development of predictive ML models for user engagement and cardiovascular risk: data exploration, feature engineering, training, validation, deployment, monitoring, A/B testing, and MLOps; write production-grade Python; apply strong statistical methods, causal inference, and advanced ML techniques; collaborate with product, data, and software teams.