דרושים » תוכנה » Experienced Machine Learning Engineer

We are looking for an experienced Deep Learning Algorithm Developer to join one of our teams developing perception technologies for autonomous driving.
Our group tackles diverse challenges - from object detection and scene understanding to 3D perception - using large-scale, real-world in-house datasets and state-of-the-art deep learning models.
What will your job look like:
Develop and train neural networks for complex perception tasks in real-world driving environments.
Design and analyze deep learning pipelines, from data to deployment.
Collaborate with algorithm, software, and data experts to deliver production-ready models.
Research and apply new methods in computer vision, 3D perception, and multi-task learning to improve performance and efficiency.

Required Experienced Deep Learning Algorithm Developer - Perception Solutions
Join our Perception Solutions team, working on significant computer vision challenges and solving them with cutting-edge neural networks combined with heuristic solutions.
In this role, you will join at the beginning of an end-to-end development cycle, leveraging advanced approaches to enable direct vision modeling beyond traditional image-processing pipelines and help advance the next generation of autonomous driving technology.
What will your job look like:
Contribute to building a new deep learning project from scratch, with real impact on production systems.
Work on end-to-end model development, including problem formulation, data understanding, model design, training, evaluation, optimization, and deployment.
Develop and train state-of-the-art deep learning models for computer vision tasks.
Explore and define the best technical approach in an open, research-driven environment, where solutions are not predefined.
Tackle real-world challenges where model performance is critical and directly impacts product capabilities.

Required Algorithm Researcher - Autonomous Driving (CTO Group)
The CTO Group is a small, elite team focused on the decision-making module of our autonomous driving system.
Our mission is to solve some of the hardest problems in autonomous driving by combining the full spectrum of modern computer science - from rigorous mathematical modeling, optimization, and algorithm design to deep learning.
The team develops the driving policies that translate system-wide inputs into safe, efficient, and human-like driving behavior. Our work spans classical algorithms, optimization under constraints, probabilistic reasoning, reinforcement & supervised learning, all grounded in real-world driving data.
The group is comprised of top researchers and developers and is led by world-class scientists, offering a unique opportunity to work closely with the CTO, shape the future of autonomous driving, and influence the company's technical direction.
What will your job look like:
Research and develop innovative algorithms for autonomous driving planning and decision-making.
Model complex real-world driving scenarios and formulate them as optimization, learning, or algorithmic problems.
Apply a broad range of techniques - from mathematical modeling and classical algorithms to machine learning and deep learning - to improve driving policies.
Analyze large-scale driving data to gain insights, validate hypotheses, and improve system robustness and performance.
Design and implement high-quality production-ready code in a research-driven environment.
Read, evaluate, and build upon state-of-the-art scientific literature in algorithms, AI, and machine learning.

We are looking for someone who brings ML and computer-vision depth to the team - someone who can help shape the intelligence layer that decides what data is worth training on.
What will your job look like:
Work collaboratively with shared ownership. Your focus area will be the curation and ML side of our data pipeline, but you will contribute across the full stack alongside the rest of the team.
Build and improve the curation pipeline - from vision-model embeddings and scene detection, through VLM-based scene analysis, to scoring, deduplication, and sampling that produces a balanced and diverse dataset.
Run and optimize GPU inference at scale (embedding extraction, VLM inference) across thousands of driving sessions using workflow orchestration.
Develop scoring and sampling strategies that ensure rare but important scenarios (night driving, adverse weather, hazardous situations) are well-represented in the final dataset.
Work with algorithm teams to understand what data gaps hurt model performance and translate those into curation criteria.
Build validation and diagnostics that measure dataset quality - not just pipeline health, but whether the data is actually good for training.
Contribute to the core dataset SDK, converter, and 3D-geometry tooling (camera projection, calibration, coordinate transforms).

We are looking for an Expert Deep Learning Research Engineer who will both drive independent deep learning research and collaborate closely with other developers to help unlock new directions, solve complex challenges, and elevate the teams algorithmic capabilities.
What will your job look like:
Conduct hands-on research and development of deep learning algorithms for road understanding, drivable area detection, and scene structure modeling
Lead exploratory investigations into new architectures, training strategies, and data utilization methods
Join forces with other developers to troubleshoot difficult problems, introduce new ideas, and guide technical decisions
Own projects end-to-end - from concept and experimentation to integration and scalable deployment.

We are looking for a Deep Learning Tech Lead for Objects Modeling, who will be challenged by solving real-world problems using Computer Vision. Our mission in the algorithms department has a significant impact on autonomous vehicle technologies, external customers, and advanced driver-assistance systems (ADAS). In this role, you will be responsible for the full cycle of deep learning R&D, including modeling new solutions and enhancing the performance of existing ones. Your responsibility will include design, implementation, guidance and mentoring for team members. You will leverage your extensive experience to lead multiple deep projects, and will be a key figure in leading successful execution and in finding creative solutions to difficult bottlenecks, across data, AI, engineering and product considerations.
This role requires working at our Jerusalem offices 1 day per week.
What will your job look like:
Lead the design, development, and implementation of accurate and robust AI algorithms for new projects
Research the latest advancements in deep learning, multi-task learning, AI architectures, and apply them to enhance our algorithm
Design, develop and implement deep learning algorithms to solve complex problems in computer vision
Take ownership of deep learning projects: Design solutions, oversee execution, and collaborate with talented Algorithm developers
Collaborate with cross-functional teams to understand requirements and develop solutions.

We are seeking a highly motivated and experienced LLM/ML Agentic AI Researcher to lead the technical development of our agentic AI interpretation framework. This hands-on role involves designing, building, and evaluating AI agents that interpret complex biological data.

You will be at the forefront of developing a sophisticated scientific reasoning system that leverages Large Language Models (LLMs) to provide structured, biologically-grounded explanations. Collaborating closely with immunologists, machine learning researchers, and technical leadership, you'll shape how we derive insights at a systems level, pushing the boundaries of AI in biology.

Location: Ramat Gan, Israel (Hybrid role)

What will you do?

Design, prototype, and build LLM-based agentic systems that reason over biological data, scientific literature, model outputs, and internal tools.
Develop agents capable of structured reasoning, hypothesis generation, explanation, planning, tool use, and iterative scientific analysis.
Build robust evaluation frameworks for agentic systems, including automated and human-in-the-loop evaluation pipelines.
Define and implement benchmarks, metrics, and test suites for measuring agent performance, including reasoning quality, biological grounding, factuality, robustness, reproducibility, and usefulness.
Work closely with AI researchers, computational biologists, immunologists, and product teams to translate scientific needs into measurable AI capabilities.
Create evaluation datasets and benchmark tasks that reflect real-world biological and therapeutic reasoning problems.
Analyze agent behavior, failure modes, hallucinations, tool-use errors, reasoning gaps, and grounding issues.
Contribute to the architecture of production-grade AI systems, including agent orchestration, retrieval, tool calling, memory, planning, and monitoring.
Stay up to date with the latest developments in LLMs, agentic AI, evaluation methodologies, and scientific AI systems.
Help turn research prototypes into reliable products used by internal teams and external partners.

we are looking for a Senior ML Engineer.
Responsibilities:
Work on core data and machine learning infrastructure, which is at the heart of offering. You will create innovative solutions for data ingestion and normalization from multiple data sources, feature engineering and feature selection, as well as actual model training and evaluation. All in large scale and completely automated.

We are looking for an AI Engineer to play a central role in building, evaluating, and advancing AI models. You will own and evolve benchmarking and evaluation capabilities for foundation models and multimodal systems, while also working closely with modeling teams to support model development, iteration, and validation. This role sits at the intersection of software engineering, model understanding, and applied AI, with broad influence on how models are built, compared, and improved across the organization.

Location: Ramat Gan, Israel (hybrid model)

What will you do?

Own & Evolve Benchmarking - Design, build, and maintain benchmarking suite for foundation models and multimodal AI systems.
Define Core Abstractions - Create clean, extensible abstractions and APIs for datasets, tasks, models, metrics, and evaluation workflows.
Develop Metrics & Evaluations - Implement metrics that capture predictive performance, biological relevance, and multimodal alignment.
Support Model Development - Work closely with AI scientists and data scientists to integrate new models, tweak architectures, and enable rapid, fair iteration.
Bring in New Models & Baselines - Add external and internal models to benchmarks and ensure meaningful comparisons.
Explore Data When Needed - Dive into data and results to debug evaluations, understand model behavior, and unblock modeling work.
Enable Rigor & Reproducibility - Ensure evaluations are consistent, well-versioned, and trustworthy over time.

Were seeking a Head of AI to lead research and engineering for multimodal foundation models and reasoning‑centric systems-from prototyping to reliable, secure, production deployment. The Head of AI will manage deep learning researchers, computational biologists, immunologists and engineers and partner closely with product leaders and company leadership to convert cutting‑edge methods into high‑impact, mission‑critical capabilities.

Location: Ramat Gan, Israel (hybrid model)

What will you do?

Team Leadership & Growth

Lead, mentor, and develop deep learning researchers, computational biologists, and machine learning engineers; set a high bar for scientific rigor, code quality, and delivery.
Establish clear ownership, role definitions, and growth paths; nurture a collaborative, low‑ego culture.
Technical Strategy & Delivery

Own the AI roadmap and architecture; guide model design, evaluation, and productionization (training, serving, observability, safety).
Build scalable pipelines for transformers, multimodal fusion, and reasoning/agent frameworks; champion reproducibility, CI/CD for models, and cost‑efficient GPU/TPU utilization.
Research Integration & Innovation

Stay current with the literature; run journal clubs and technical deep dives.
Evaluate, pilot, and integrate state‑of‑the‑art methods (advanced transformers, retrieval and tool‑use agents, causal/biological reasoning) into robust systems.
Cross‑Functional Collaboration

Translate biological and clinical questions into tractable AI projects with measurable impact.
Communicate complex trade‑offs to peers and executives; align plans, risks, and milestones across functions.
Mission & Impact

Prioritize initiatives that advance patient outcomes and create cumulative platform value.
Balance speed with scientific integrity, security, and compliance.