Gen AI Staff Researcher

Job Description

GM Israel (Herzliya) takes a significant part in introducing sophisticated software features into car in many domains like connected cars, advanced camera technologies and research. We shape the face of the future vehicles in diverse fields by developing cutting edge technologies that define how drivers experience safety, awareness, and confidence on the road.

GM is committed to Zero Emission, Zero Crashes and Zero Congestion vision.

About the Group

At the autonomous vehicles Foundational AI Research team in Israel, which is part of the GM AV AI Research organization, we are shaping next-generation autonomous driving, L3 and beyond. The AI Research organization is dedicated to advancing the state of the art in AI for autonomous vehicles. We are a collaborative, forward-thinking group of researchers and engineers tackling some of the most complex challenges in autonomy and machine learning.

About the Role

As a Gen AI Staff Researcher, you will contribute deep technical expertise to a collaborative research team developing generative world models, with a focus on the design and large-scale training of diffusion models for autonomous driving. You will research and develop models that learn the dynamics of driving environments and generate realistic, temporally consistent, and controllable future scenarios, conditioned on sensor observations, actions, and scene context.

You will contribute hands-on throughout the research lifecycle, from exploring new ideas to large-scale experimentation and implementation, advancing model architectures, training objectives, data strategies, and evaluation methods. As an expert member of the team, you will bring scientific rigor, share technical knowledge, and work closely with fellow researchers and partners in Perception, Behavior/Policy, Data, Simulation, and Validation to translate generative AI advances into measurable improvements in autonomy KPIs. You will also contribute high-quality publications at top-tier conferences and journals and collaborate with leading academic partners.

At the GM Foundational AI Research group, your work will influence millions of drivers worldwide and define what’s next in autonomous driving. If you're passionate about solving hard problems and advancing the frontiers of generative AI and autonomous driving, we want you on our team.

What will YOU do?

• Contribute original research and technical expertise to generative world models, with a focus on diffusion-based modeling of driving environments and their dynamics.

• Design, build, train, and evaluate large-scale diffusion models for video and multimodal generation, emphasizing temporal consistency, physical plausibility, and controllability.

• Implement and optimize large-scale training on distributed, multi-GPU infrastructure, working with fellow researchers and engineering partners to improve training stability, throughput, memory efficiency, and scalability.

• Develop action- and context-conditioned world models that generate diverse future scenarios and support counterfactual exploration, closed-loop simulation, and long-tail scenario coverage.

• Develop and evaluate data and training strategies for fleet-scale datasets together with the team, including data curation, pretraining, fine-tuning, and targeted generation of challenging driving scenarios.

• Develop and apply rigorous evaluation methods that connect generative quality, consistency, and scenario coverage to downstream KPI improvements for the autonomous driving agent.

• Collaborate with engineering teams to translate state-of-the-art research into scalable solutions for simulation, synthetic data generation, and autonomy development.

• Publish research at top-tier conferences and journals, track emerging advances in generative AI, and incubate technologies aimed at impacting our L3 autonomous driving technology.

Your Skills & Abilities (Required Qualifications)

• PhD in Computer Science, Electrical Engineering, Robotics, or a related field. Excellent M.Sc. graduates will be considered.

• Over 3 years of research experience in generative AI, computer vision, machine learning, or related areas, with demonstrated depth of expertise and impactful contributions to complex research projects.

• Deep expertise in diffusion models and hands-on experience designing, training, and evaluating generative models for images, video, or multimodal data.

• Hands-on experience with large-scale distributed training of deep learning models, including training stability, performance optimization, and systematic experimentation.

• Strong understanding of world modeling and spatiotemporal learning, including temporal consistency, conditioning mechanisms, and learning environment dynamics.

• Strong publication record at top-tier AI/ML conferences and journals.

• Excellent coding skills in Python and proficiency with modern AI frameworks such as PyTorch.

About GM

Our vision is a world with Zero Crashes, Zero Emissions and Zero Congestion and we embrace the responsibility to lead the change that will make our world better, safer and more equitable for all.

Why Join Us

We believe we all must make a choice every day – individually and collectively – to drive meaningful change through our words, our deeds and our culture. Every day, we want every employee to feel they belong to one General Motors team.

Non-Discrimination and Equal Employment Opportunities

General Motors is committed to being a workplace that is not only free of unlawful discrimination, but one that genuinely fosters inclusion and belonging. We strongly believe that providing an inclusive workplace creates an environment in which our employees can thrive and develop better products for our customers.

We encourage interested candidates to review the key responsibilities and qualifications for each role and apply for any positions that match their skills and capabilities. Applicants in the recruitment process may be required, where applicable, to successfully complete a role-related assessment(s) and/or a pre-employment screening prior to beginning employment. To learn more, visit How we Hire.

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