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12 Jan 2021

Full-Time Senior Software Engineer (Fullstack), Machine Assisted Cognition – Posted by Los Altos, California, United States

Job Description

At Toyota Research Institute (TRI), we’re working to build a future where everyone has the freedom to move, engage, and explore with a focus on reducing vehicle collisions, injuries, and fatalities. Join us in our mission to improve the quality of human life through advances in artificial intelligence, automated driving, robotics, and materials science. We’re dedicated to building a world of “mobility for all” where everyone, regardless of age or ability, can live in harmony with technology to enjoy a better life. Through innovations in AI, we’ll help…
– Develop vehicles incapable of causing a crash, regardless of the actions of the driver.
– Develop technology for vehicles and robots to help people enjoy new levels of independence, access, and mobility.
– Bring advanced mobility technology to market faster.
– Discover new materials that will make batteries and hydrogen fuel cells smaller, lighter, less expensive and more powerful.
– Develop human-centered AI systems to augment (not replace) human decision making to increase the quality of decisions (e.g. mitigate cognitive biases) and/or to facilitate faster innovation cycles.
Our work is guided by a dedication to safety – in both what we research and how we perform our research our goal is to benefit society. As a subsidiary of Toyota, TRI is fueled by a diverse and inclusive community of people who carry invaluable leadership, experience, and ideas from industry-leading companies. Over half of our technical team carries PhD degrees. We’re continually searching for the world’s best talent ‒ people who are ready to define the new world of mobility with us!
We strive to build a company that helps our people thrive, achieve work-life balance, and bring their best selves to work. At TRI, you will have the opportunity to enjoy the best of both worlds ‒ a fun environment with forward-thinking people who enjoy solving tough problems and the financial backing to successfully achieve our goals. Come work with TRI if you’re interested in transforming mobility through designing technology for safer cars, enabling the elderly to age in place, or designing alternative fuel sources. Start your impossible with us.
TRI’s Machine Assisted Cognition team is developing AI systems to augment (not replace) human decision making. In particular, we are interested in advancing the intersection between behavioral science, machine learning and causal inference to increase the quality of human decisions (e.g. mitigate cognitive biases) and / or to facilitate faster innovation cycles. We are looking for a Staff Software Engineer to join this exciting new endeavor to collaborate cross-functionally with machine learning and causal inference experts, behavioral scientists, designers, user researchers, and other software engineers.


    • Lead the design and development of software for the machine-assisted cognition (MAC) team.
    • Collaborate cross-functionally with machine learning and causal inference experts, designers, behavioral scientists, user researchers, and university partners to design and build novel AIs to augment human decision making.
    • Promote software engineering best practices that produce maintainable code, including great design, automated testing, continuous integration, code style consistency and code review.
    • Mentor and advise others.


    • Bachelor’s or Master’s degree in Computer Science or equivalent.
    • Deep expertise in architecting and building scalable distributed systems.
    • Experience developing user-facing, interactive, real-time products preferred.
    • Proficiency in machine learning.
    • Experience in providing technical leadership and mentorship.
    • Strong track record of driving and executing complex, open-ended, cross-functional projects.
    • Strong interpersonal skills. Great teammate.
    • Appetite to learn across functions.
Please add a link to Google Scholar and include a full list of publications when submitting your CV to this position.

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Job Types: Full-Time.

Job expires in 75 days.

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