At Toyota Research Institute (TRI), we’re on a mission to improve the quality of human life. We’re developing new tools and capabilities to amplify the human experience. To lead this transformative shift in mobility, we’ve built a world-class team advancing the state of the art in AI, robotics, driving, and material sciences. This is a paid 12-week internship opportunity and is a hybrid, in-office role. Here’s a glimpse into the Internship experience from some of our TRI interns! The Team The Platform Research team, part of the Human Interactive Driving (HID) division, is dedicated to developing advanced research platforms. These platforms are crucial for enhancing vehicle system design and seamlessly integrating hardware and software. Our objective is to accelerate innovation, ultimately creating safer and more enjoyable driving experiences. We aim to achieve a "Software First" approach by designing hardware platforms with unified architectures and streamlined, agile integration processes. This methodology facilitates rapid prototyping and iterative testing of cutting-edge concepts. The Internship TRI is seeking System Integration Engineering Interns to play a key role in envisioning, designing, and developing cutting-edge research platforms. As part of our integrated engineering team, you will collaborate with multidisciplinary teams across TRI to craft innovative solutions. This role offers the opportunity to contribute to engineering projects and programs at every stage of development, from concept to execution. What You’ll Gain: Hands-on Learning: Practical experience working on groundbreaking technologies. Global Perspective: Exposure to diverse, cross-functional collaborations. Professional Growth: Access to networking, mentorship, and development resources to advance your technical and career skills. Industry Expertise: Work alongside seasoned professionals contributing to impactful initiatives. Responsibilities • Contribute to the design and integration of an AI monitoring system for custom vehicle platforms. • Explore how new data modalities such as audio and video fit into the vehicle monitoring system alongside CAN data. • Deploy and run monitoring and AI models on in-vehicle edge computers. • Assist in the design and integration of hardware and software systems. • Collaborate with our multidisciplinary teams to learn and address integration challenges. • Document integration processes, procedures, and outcomes for future reference and improvement. • Contribute to the continuous enhancement of our integration processes and methodologies. • Proactively engage in self-improvement and skill development, staying updated with the latest technologies and industry best practices. • Approach tasks with enthusiasm and a commitment to achieving project objectives while fostering a positive and collaborative attitude. • Communicate effectively with team members and other departments, promoting an open-minded and cooperative work environment. Qualifications • Currently pursuing a Master degree in Mechanical Engineering, Electrical Engineering, Computer Science, Robotics, or a related field. • A strong interest in gaining experience in the integration of mechanical, electrical, and software systems. • Proficiency in Python, including experience with data analysis libraries (NumPy, pandas) and writing tested code. • Experience with embedded Linux and edge AI hardware, ideally NVIDIA Jetson (JetPack, CUDA, TensorRT/ONNX Runtime, Docker on ARM64). • Familiarity with C++,  MATLAB/SIMULINK, Network (Ethernet/CAN). • Experience working with CAD is preferred. • Demonstrated self-motivation and a strong desire for continuous learning and skill development. • An eager and humble attitude with a commitment to contributing to project goals. • An open-minded and adaptable approach to problem-solving. • Good communication skills with the ability to work effectively within cross-functional teams. • Strong problem-solving and troubleshooting aptitude. Bonus Qualifications • Coursework or project experience in machine learning and/or building with LLMs (prompting, tool/function calling, evaluation). • Optimizing models for edge inference (quantization, TensorRT, DeepStream). • Knowledge of ROS or other relevant programming skills. • Familiarity with the safety-critical system design process (FMEA, FTA). The pay range for this position at commencement of employment is expected to be between $45 and $65/hour for California-based roles. Base pay offered will depend on multiple individualized factors, including, but not limited to, a candidate's experience, skills, job-related knowledge, and market location. TRI offers a generous benefits package including medical, dental, and vision insurance, and paid time off benefits (including holiday pay and sick time). Additional details regarding these benefit plans will be provided if an employee receives an offer of employment.   Please reference this Candidate Privacy Notice to inform you of the categories of personal information that we collect from individuals who inquire about and/or apply to work for Toyota Research Institute, Inc. or its subsidiaries, including Toyota A.I. Ventures GP, L.P., and the purposes for which we use such personal information.   TRI is fueled by a diverse and inclusive community of people with unique backgrounds, education and life experiences. We are dedicated to fostering an innovative and collaborative environment by living the values that are an essential part of our culture. We believe diversity makes us stronger and are proud to provide Equal Employment Opportunity for all, without regard to an applicant’s race, color, creed, gender, gender identity or expression, sexual orientation, national origin, age, physical or mental disability, medical condition, religion, marital status, genetic information, veteran status, or any other status protected under federal, state or local laws.   It is unlawful in Massachusetts to require or administer a lie detector test as a condition of employment or continued employment. An employer who violates this law shall be subject to criminal penalties and civil liability. Pursuant to the San Francisco Fair Chance Ordinance, we will consider qualified applicants with arrest and conviction records for employment.