Bosch Group
Research Engineer- End-to-End Planning in Autonomous Systems
Company
Bosch Group
Job type
Full-time
Location
Sunnyvale, CA, us
Posted
2 hours ago
Salary
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As a Research Scientist / Engineer for End-to-End Planning in Autonomous Systems, you contribute to research projects at the forefront of the ADAS/AD industry. Key responsibilities include:
- Conduct research and engineering in core AI and machine learning fields to enable Embodied AI (including computer vision, autonomous planning, open-world learning, and so on) for related business domains of ADAS/AD, industrial automation, robotics etc.
- Push the boundaries in (modular) end-to-end perception and planning for ADAS/AD, incorporating advancements in large vision-language-(action) models to aid reasoning capabilities and explainability.
- Collaborate cross-functionally with global research and engineering teams to ensure seamless technology transfer and system integration.
- Implement research results to solve real-world challenges, ensuring high-quality system integration within Bosch's existing platforms.
- Stay at the forefront of innovation by actively engaging with academic and industry communities through conferences, workshops, and technical events.
- Document and disseminate research findings through high-caliber publications and/or patent submissions.
Basic Qualifications
- Ph.D. in Computer Science, Robotics or a related discipline or Master's degree with >= 1 / 3 years industry experience after graduation.
- A minimum of 3 years of R&D experience, or an equivalent graduate research background, primarily in AI technologies including Computer Vision and Robotic or Automotive Motion and Behavioral Planning.
- Proficiency in one or more programming languages commonly used in machine learning (e.g., Python, C++, Rust).
- Strong interpersonal, communication, and teamwork capabilities.
- Knowledge of major machine learning frameworks like TensorFlow or PyTorch.
- Hands-on experience in reinforcement learning for behavior or motion planning or other applicable contexts and familiarity with common RL techniques (e.g. PPO, DQN, DDPG).
Preferred Qualifications
- Experience with real-world product development and deployment of autonomous systems.
- Hands-on experience building and applying multimodal transformer-based sequence-to-sequence models, especially multimodal vision-language-action models.
- Hands-on experience in computer vision and deep learning, with work in any of the following areas: multimodal transformers, multimodal language models, diffusion models, NeRF, gaussian splatting, object detection / segmentation, 3D scene understanding, sensor calibration, SfM, voxel/BEV grid-based feature representation.
- A strong portfolio of publications in premier machine learning, deep learning, robotics and computer vision journals and conferences.
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: https://www.myboschbenefits.com/public/welcome.
Equal Opportunity Employer, including disability / veterans.
*Bosch adheres to Federal, State, and Local laws regarding drug-testing. Employment is contingent upon the successful completion of a drug screen and background check. Candidates who have been offered the position must pass both screenings before their start date.
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