Top Machine Learning Jobs in Israel

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.

Design, develop, and deploy machine learning models for risk, fraud, and operations. Operate training/deployment infrastructure, analyze multiple data sources, conduct model impact research, and integrate models into production backend systems to improve portfolio performance.

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.

Lead development of AI-driven verification tooling: build AI ecosystems, integrate ML/LLM agents into verification flows, automate stimulus generation and coverage closure, create intelligent debug assistants, research AI approaches to state-space explosion, and train teams on human-in-the-loop methodologies.

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, implement, and productionize ML models and optimization algorithms for Via's real-time dispatch system. Build online decision-making and reinforcement-learning solutions, model operational tradeoffs, analyze demand and traffic patterns, and collaborate with engineering, product, and operations to deploy and iterate at scale.

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.

Design and build production-grade autonomous agentic systems for supply chain problems. Architect multi-agent workflows, integrate ML/ deep learning models, evaluate agent performance, and collaborate with engineering and product teams to productize, monitor, and iterate agent pipelines.

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 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.

The role involves developing AI-enhanced methodologies for pre-silicon functional verification, engaging with customers, and collaborating with engineering teams.

The role involves applying machine learning to optimize verification methodologies, specifically for ASIC pre-silicon verification, collaborating with teams, and engaging with customers on innovative strategies.

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.