Senior Data Scientist

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 Energy & Materials, Human-Centered AI, Human Interactive Driving, Large Behavioral Models, and Robotics.

The Discover, Nurture, and Adopt (DNA) division at TRI focuses on enabling innovation and transformation at Toyota by building a bridge between TRI research and Toyota products, services, and needs. We achieve this through partnership, collaboration, and shared commitment. DNA is leading a new cross-organizational project between TRI and Woven by Toyota to research and develop a fully end-to-end learned automated driving / ADAS stack. This cross-org collaborative project is harmonious with TRI’s robotics divisions' efforts in Diffusion Policy and Large Behavior Models (LBM).

We are looking for a data scientist to contribute to the end-to-end model development using Diffusion Policy and Large Behavior Models (LBM) for automated driving. This role involves focusing on analyzing large-scale data, evaluating models, curating datasets, and optimizing performance to enhance automated driving systems. You will also develop algorithms, pipelines and key strategies to enhance and streamline scalable large fleet data and vendor data. You will work closely with machine learning researchers, software engineers, and roboticists to drive execution, manage dependencies, and accelerate the transition of research models into real-world deployment.

This role offers a unique opportunity to drive innovative AI research into real-world deployment, shaping the future of automated driving. If you are passionate about building transformative AI-driven mobility solutions, we invite you to apply and help us advance the next generation of automated driving vehicle technology.

Responsibilities

• Design and implement data strategies for collecting, sampling, labeling, and using large-scale datasets to train and validate Large Behavior Models (LBM) in automated driving scenarios.
• Develop metrics and evaluation frameworks for anomaly detection and trend analysis in data from various sources, including real-world vehicle platforms and simulations.
• Analyze model performance metrics, model failure modes, statistical relevance of datasets, collaborating with machine learning engineers to fine-tune, debug, and optimize models for automated driving tasks.
• Design and improve scalable data pipelines and automation for machine learning and performance evaluation.
• Support the integration of multi-modal data sources (e.g., vision, radar, language, maps) into end-to-end driving models.
• Work closely with cross-functional teams to bridge the gap between research models and production deployment.

Qualifications

• MS or PhD in related fields.
• 5+ years of experience in data science, machine learning, or a related field, with a focus on large-scale AI applications.
• Strong background in statistical analysis, data mining, and machine learning techniques.
• Proficiency in Python and SQL for data manipulation, analysis, and visualization.
• Experience with theoretical aspects of data science and machine learning (deep learning, statistical analysis, and mathematical modeling).
• Experience in building machine learning algorithms and infrastructure, including data pre- and post-processing, sampling and curation, ablation studies, and evaluation.
• Experience with deep learning frameworks (e.g., PyTorch, TensorFlow) and ML model evaluation.
• Familiarity with large-scale dataset management, including handling high-dimensional sensor data.
• Strong analytical and problem-solving skills, with the ability to interpret complex datasets and drive actionable insights.
• Experience working in cross-functional AI research and engineering teams.

Bonus Qualifications

• Experience in automated driving technologies, including perception, prediction, planning, or sensor simulation.
• Experience in building or managing infrastructure, such as Docker, Kubernetes, Jenkins, GitHub Actions.
• Experience working with temporal/sequential and/or spatial data.
• Hands-on experience with sensor data processing (e.g., camera, LiDAR, radar, IMU).
• Familiarity with automotive simulation tools and real-world autonomous vehicle testing.

The pay range for this position at commencement of employment is expected to be between $201,600 and $302,400/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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