Job description

• Lead strategic research and engineering initiatives in AI safety, alignment, and model reliability, with direct application to ADAS feature releases and autonomous systems. • Advance generative world models, simulation frameworks, and physical reasoning techniques to evaluate complex driving edge cases and validate system behavior. • Collaborate with global R&D teams to seamlessly transfer cutting-edge safety frameworks and machine learning algorithms into operational platforms and production pipelines. • Implement rigorous safety validation, out-of-distribution (OOD) detection, failure mode analysis, and uncertainty estimation methods to ensure robust platform integration. • Monitor emerging developments in foundation models and safety alignment, representing the organization by presenting at top-tier conferences (e.g., NeurIPS, CVPR, ICLR, ICRA) and publishing high-impact research. • Provide expert technical counsel to senior leadership to guide strategic R&D roadmaps, investment decisions, and patent portfolio growth in trustworthy AI. Basic Qualifications • Ph.D. in Computer Science, Machine Learning, Robotics, Artificial Intelligence, or a related quantitative field. • 3+ years of dedicated research experience (academic or industry) focusing on world models, generative dynamics, self-supervised learning, or physical reasoning for autonomous systems. • 2+ years of industrial R&D experience in creating, fine-tuning, and deploying large-scale machine learning models and safety-critical algorithms into production or testing environments. • Hands-on engineering proficiency in Python and deep learning frameworks (PyTorch or JAX). Preferred Qualifications • Deep theoretical and practical expertise in AI safety techniques, including alignment, adversarial robustness, interpretability (XAI), failure mode analysis, and uncertainty quantification. • Strong low-level engineering experience with C++ and high-performance distributed training pipelines for system integration. • Proven track record of peer-reviewed publications in top-tier machine learning or robotics venues (e.g., NeurIPS, CVPR, ICCV, ECCV, ICLR, ICRA). • Demonstrated experience generating intellectual property and securing patent contributions in trustworthy AI or autonomous systems. We offer a competitive base salary for this position with a range in US-California of --$165,000 - $185,000 along with an annual corporate bonus, and a long-term incentive bonus designed to reward sustained impact and contribution over time. Within the salary range, the individual pay is determined based on several factors, including, but not limited to, work experience and job knowledge, complexity of the role, job location, etc. Your well-being matters at Bosch! We offer a a benefits package designed to empower you in every area of your life. This includes premium health coverage, a 401(k) with generous matching, resources for financial planning and goal setting, ample paid time off, parental leave, and comprehensive life and disability protection. Your Recruiter can share more details for this position during the interview process. Learn more about our full benefits offerings by visiting: myboschbenefits.com . Equal Opportunity Employer, including disability / veterans. #LI-JM1 Culture and Benefits | Bosch in the USA

Requirements

Must-haves

Lead strategic research and engineering initiatives in AI safety, alignment, and model reliability, with direct application to ADAS feature releases and autonomous systems.
Advance generative world models, simulation frameworks, and physical reasoning techniques to evaluate complex driving edge cases and validate system behavior.
Collaborate with global R&D teams to seamlessly transfer cutting-edge safety frameworks and machine learning algorithms into operational platforms and production pipelines.
Implement rigorous safety validation, out-of-distribution (OOD) detection, failure mode analysis, and uncertainty estimation methods to ensure robust platform integration.
Monitor emerging developments in foundation models and safety alignment, representing the organization by presenting at top-tier conferences (e.g., NeurIPS, CVPR, ICLR, ICRA) and publishing high-impact research.
Provide expert technical counsel to senior leadership to guide strategic R&D roadmaps, investment decisions, and patent portfolio growth in trustworthy AI.
Ph.D. in Computer Science, Machine Learning, Robotics, Artificial Intelligence, or a related quantitative field.
3+ years of dedicated research experience (academic or industry) focusing on world models, generative dynamics, self-supervised learning, or physical reasoning for autonomous systems.
2+ years of industrial R&D experience in creating, fine-tuning, and deploying large-scale machine learning models and safety-critical algorithms into production or testing environments.
Hands-on engineering proficiency in Python and deep learning frameworks (PyTorch or JAX).
Culture and Benefits | Bosch in the USA

Nice-to-haves

Deep theoretical and practical expertise in AI safety techniques, including alignment, adversarial robustness, interpretability (XAI), failure mode analysis, and uncertainty quantification.
Strong low-level engineering experience with C++ and high-performance distributed training pipelines for system integration.
Proven track record of peer-reviewed publications in top-tier machine learning or robotics venues (e.g., NeurIPS, CVPR, ICCV, ECCV, ICLR, ICRA).
Demonstrated experience generating intellectual property and securing patent contributions in trustworthy AI or autonomous systems.
Equal Opportunity Employer, including disability / veterans.

Application prep

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Keywords to mirror

aimachine learningpython

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Effort: medium

Benefits

Benefits detected in the listing.

401(K)

Paid Time Off

Parental Leave