Automotive J-1 Sponsorship Jobs in California

Company Description

The Bosch Research and Technology Center North America with offices in Sunnyvale, California, Pittsburgh, Pennsylvania and Cambridge, Massachusetts is a part of the global Bosch Group, a company with over 80-billion-euro revenue, 400,000 employees worldwide, a very diverse product portfolio, and a history spanning over 125 years. The Research and Technology Center North America (RTC-NA) is dedicated to providing technologies and system solutions for various Bosch business fields, primarily in the field of artificial intelligence (AI) (e.g., human-assisted AI, natural language processing, robotics, 3D perception, and AI platform), energy technologies, internet technologies, circuit design, semiconductors and wireless, as well as advanced MEMS design.

As a part of the global research, our AI research in Silicon Valley (Bosch Center for AI) focuses on research topics such as Big Data Visual Analytics, Explainable AI (XAI), Natural Language Processing, 3D Perception & Mixed Reality, Cloud Robotics, Data Science, AI Platform, Time-series Analysis. We develop scalable, intelligent, and trustworthy AIoT solutions to enable inspiring UX for Bosch products and services in application areas such as automated driving, advanced driver assistance systems (ADAS), robotics, smart manufacturing, enterprise AI, health care, smart home and building solutions.

We Are Bosch.

At Bosch, we shape the future by inventing high-quality technologies and services that spark enthusiasm and enrich people’s lives. Our areas of activity are every bit as diverse as our outstanding Bosch teams around the world. Their creativity is the key to innovation through connected living, mobility, or industry.

Let’s grow together, enjoy more, and inspire each other. Work #LikeABosch

• Reinvent yourself: At Bosch, you will evolve.
• Discover new directions: At Bosch, you will find your place.
• Balance your life: At Bosch, your job matches your lifestyle.
• Celebrate success: At Bosch, we celebrate you.
• Be yourself: At Bosch, we value values.
• Shape tomorrow: At Bosch, you change lives.

Job Description

The ideal candidate will passionately drive and further advance innovations in the field of reinforcement learning and simulation for planning in autonomous driving. The work includes conducting advanced research and development of novel algorithms and conducting experiments to bring innovation ideas to products.

As an intern, you will work closely with a team of researchers and engineers on one of the following areas:

1. GPU-accelerated Simulation for Reinforcement Learning

   Build and optimize high-performance, scalable simulation environments tailored for reinforcement learning in autonomous driving scenarios.

2. Integration and Development of ML-based Planning Models

   Develop, train, and integrate planning models for autonomous driving using GPU-accelerated simulations to validate and enhance performance in complex scenarios.

3. Imitation + Reinforcement Learning with Self-Play

   Develop and improve hybrid learning approaches that combine imitation learning and reinforcement learning, with a focus on multi-agent self-play techniques.

Responsibilities include but not limited to:

• Participate in cutting-edge engineering projects applying deep learning and reinforcement learning to tackle challenges in planning and simulation for autonomous driving contexts.
• Work with an international team of experts to transfer the results of advanced research to Bosch business units. Benchmark, validate and test research ideas on simulated environments, large scale datasets, and self-driving vehicles.
• Collaborate with a team of domain experts on novel approaches to learning-based planning and decision-making.
• Benchmark, validate, and iterate on models using large-scale simulation and datasets.
• Communicate research findings through internal reports and/or external publications.

Basic Qualifications

• Currently pursuing MS or PhD in Computer Science / Robotics / Systems Engineering or a related technical field, with research focus on high-performance simulation, reinforcement learning, robotic systems, or autonomous driving applications.
• Hands-on experience in deep learning and/or AI system topics with focus on at least two of the following areas: reinforcement learning, vector/point-based input representations for learning, planning for navigation, multi-agent training / self-play, and autonomous driving.
• Programming experience in C++, Python, and hands-on experience with libraries such as PyTorch, CUDA, Tensorflow, etc.
• Minimum GPA of 3.0

Preferred Qualifications

• Publication record in top venues in robotics/machine learning/computer vision, e.g., ICRA, IROS, RSS, NeurIPS, ICML, ICLR, CVPR, ICCV, and ECCV.
• Project experience in the field of planning or simulation for automated driving.
• Strong leadership skills with excellent English communication & teamwork skills.
• Background in high-performance simulation, reinforcement learning, or machine learning for autonomous driving.
• Background in probabilistic robotics.
• Experience in writing algorithms in C++ efficiently and correctly in a production environment (code reviews, unit tests, etc.).
• Experience with the Madrona engine, GPUDrive, or other GPU accelerated simulation frameworks.
• Knowledge of Linux, and development on Linux systems.

Additional Information

Equal Opportunity Employer, including disability / veterans

The U.S. base salary range for this full-time position is $42 - $58/ HR. Within the range, 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 Recruiter can share more details about the specific salary range for this position during the interview process.

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.