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Toyota Research Institute

Machine Learning Researcher, Carbon Neutrality

Toyota Research Institute, Los Altos, California, United States, 94024


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 in Human-Centered AI, Energy and Materials, Machine Learning, and Robotics.

TRI's Carbon Neutrality Department within our Human-Centered AI (HCAI) Division blends behavioral science, human-computer interaction research, and machine learning to understand, predict and enable carbon neutral behavior.

We are looking for a Machine Learning Researcher with expertise and passion for foundation models, as well as training and fine-tuning on large, industrial-scale datasets. This researcher would work to define new lines of research in the generative AI space in relation to training and fine-tuning models representing human behaviors and cognitive processes, such as changes in beliefs and preferences over time. This researcher may also apply data science expertise to inform carbon neutral policy and strategy decisions.

Responsibilities

Conduct daring research, primarily in the area of generative AI models, that solves open problems of high practical and/or ethical value and validate it in real-world benchmarks and systemsPush the boundaries of knowledge and the state of the art in Human-Centered AI, including: NLP, multi-modal models, and time-varying language modelsStay up to date on the state-of-the-art in Machine Learning theories, practice, and softwareConduct exploratory analyses with large datasets to identify areas of opportunity and address strategic questionsCollaborate with scientists in the Carbon Neutrality Department to craft and shape our research program and to communicate research to Toyota partnersPublish findings in academic journals and/or conferencesContribute to technology transfer of research throughout ToyotaQualifications

PhD in natural language processing, computer vision, machine learning, or related field3+ years of experience in machine learning research or related projectsExperience with generative AI modelsBroad knowledge of machine learning approaches and theoryCapable of working collaboratively across subject areas and functionsDesire to work on challenging open-ended research projectsDemonstrated ability to work autonomously while soliciting feedbackStrong interpersonal skills and an excellent teammateStrong data science skills. Proficiency in R and/or PythonProficiency in other big data languages and tools for cloud environments (e.g., Databricks, SQL, PySpark)Ability to communicate sophisticated concepts clearly across different audiencesPlease include a link to your Google Scholar pageBonus Qualifications

Experience with language transformers, vision transformers, diffusion models, or multi-modal modelsAbility to balance multiple projects, including short-term, targeted analyses and novel, innovative projects that may span years

The pay range for this position at commencement of employment is expected to be between $151,800 and $210,000/year for California-based roles; however, base pay offered may vary depending on multiple individualized factors, including market location, job-related knowledge, skills, and experience. Note that TRI offers a generous benefits package (including 401(k) eligibility and various paid time off benefits, such as vacation, sick time, and parental leave) and an annual cash bonus structure. Details of participation in these benefit plans will be provided if an employee receives an offer of employment.

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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.