Research Scientist, World Models – Policy Training and Evaluation at Toyota Research Institute

Los Altos, California, United States

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Not SpecifiedCompensation

Senior (5 to 8 years)Experience Level

Full TimeJob Type

UnknownVisa

Automotive, Artificial Intelligence, RoboticsIndustries

Requirements

PhD in Computer Science, Robotics, Machine Learning, or a related field
Strong background in at least two of the following areas: World models or model-based reasoning in dynamic environments, World model adaptation and fine-tuning, Offline RL or imitation learning, Model-based reinforcement learning (MBRL), Simulation-to-reality transfer, or Policy evaluation and safety assurance
A track record of high-quality publications in ML or robotics venues (e.g., ICML, ICLR, NeurIPS, CoRL, RSS)
Familiarity with latent dynamics models (e.g., Dreamer, PlaNet, MuZero)
Understanding of uncertainty modeling, generalization, and robustness in learned environments
Experience evaluating autonomous vehicle policies in simulation and real-world settings
Experience in building or applying models for downstream evaluation of autonomous systems
Proficiency in Python and ML frameworks (e.g., PyTorch, JAX)

Responsibilities

Develop and refine world models that support realistic and diverse counterfactual reasoning, scenario generation, and policy rollout
Ensure that world models are compatible with and useful for reinforcement learning, imitation learning, and offline policy evaluation techniques
Design methods to synthesize high-risk or edge-case scenarios from world models, enabling robust stress-testing of autonomous policies
Explore techniques such as latent-space simulation, world model distillation, differentiable simulation, and closed-loop evaluation to improve policy development and evaluation pipelines
Partner with researchers in world modeling, planning, and safety evaluation to co-develop aligned architectures and learning objectives to ensure that learned models accurately capture agent-environment dynamics relevant to long-horizon planning and safety-critical decision-making
Publish high-quality research and contribute to the community through open-source tools, benchmarks, and conference participation

Skills

Key technologies and capabilities for this role

World ModelsReinforcement LearningImitation LearningModel-Based RLPerceptionCounterfactual ReasoningScenario GenerationPolicy OptimizationMulti-Agent ReasoningAutonomous Driving

Questions & Answers

Common questions about this position

What qualifications are required for this Research Scientist role?

A PhD in Computer Science, Robotics, Machine Learning, or a related field is required, along with a strong background in at least two of the following areas: World models or model-based reasoning in dynamic environments, World model adaptation and fin.

What is the salary or compensation for this position?

This information is not specified in the job description.

Is this a remote position, or is there a location requirement?

This information is not specified in the job description.

What does the team at Toyota Research Institute work on?

The Human Interactive Driving division's Extreme Performance Intelligent Control department develops scalable, human-like driving intelligence by learning from expert human drivers, focusing on configurable, data-driven world models for multi-agent reasoning in dynamic driving environments.

What makes a strong candidate for this Research Scientist position?

Candidates with a PhD in a relevant field and strong expertise in at least two areas like world models, model-based reasoning, or world model adaptation, plus experience in techniques such as reinforcement learning, imitation learning, and publishing high-quality research, stand out.

Toyota Research Institute

Research in mobility, safety, and automation

About Toyota Research Institute

Toyota Research Institute focuses on improving mobility through research and development in the automotive and technology sectors. The company works on enhancing safety, automated driving, robotics, materials science, and machine learning. Their products include advanced safety features and automated driving systems that aim to make driving safer and more efficient. Unlike many competitors, TRI emphasizes a research-driven approach, collaborating with various partners and licensing their innovations to enhance Toyota's offerings and maintain a competitive edge. The goal of TRI is to advance mobility solutions that improve quality of life and support the transition to zero-emissions transportation.

Los Altos, CaliforniaHeadquarters

2016Year Founded

$100MTotal Funding

SEEDCompany Stage

Robotics & Automation, Automotive & Transportation, AI & Machine LearningIndustries

201-500Employees

Benefits

Highly competitive benefits package

Robust programs to support the wellbeing, happiness, and health of our people and their families.

401(k) plan including matching and annual profit sharing along with total vacation and holidays totaling 38 days per year.

Subsidized commuter benefits and generous employee and vehicle allowances

2 paid days per year to participate in volunteer activities.

Maternity Leave program with 10 paid weeks plus baby bonding leave and Milk Stork for traveling moms

Baby Bonding Leave–an additional 16 paid weeks–to all new parents, including those who choose to adopt.

Back-up child and adult / elder care programs to help everyone thrive

WellBeats Virtual Fitness Trainer.

Risks

Public skepticism may undermine AI-assisted driving safety initiatives.

Boston Dynamics' high costs could impact TRI's humanoid robot project returns.

Complexity of real-world environments may delay TRI's robot skill deployment.

Differentiation

TRI focuses on AI to enhance human life and mobility.

Partnerships with Boston Dynamics and Stanford showcase TRI's collaborative innovation.

TRI's Driving Sensei concept integrates AI to improve driver skills and engagement.

Upsides

Generative AI allows TRI's robots to learn complex tasks without new coding.

TRI's AI-driven material synthesis could revolutionize EV battery development.

Autonomous tandem drifting collaboration with Stanford pushes vehicle dynamics boundaries.

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