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Salary
$49k – $103k per year (Estimated)
Location
Remote/Hybrid (Tokyo, Japan)
Seniority
Senior · 3+ years exp
Overview
Company
Impact
Profile match
Woven by Toyota is the primary mobility technology subsidiary of Toyota Motor Corporation, headquartered in Tokyo, Japan. Established in 2018 and rebranded to its current name in 2023, the organization acts as Toyota's software engine and tech innovation lab, driving the automaker's transition from a traditional vehicle manufacturer into a global mobility company.

TEAM

    The Behavior team at Woven by Toyota tackles Autonomy challenges for problems in behavior planning. Our work involves a variety of challenges, such as analyzing petabytes of multimodal driving data, solving optimization problems, minimizing latency on hardware accelerators, deploying scalable and efficient machine learning (ML) training and evaluation pipelines, and designing novel neural network architectures to advance state-of-the-art ML for Behavior Planning. We are looking for driven and creative problem solvers to join us in improving mobility for everyone with human-centered automated driving solutions for personal and commercial applications.

WHO ARE WE LOOKING FOR?

    The Behavior team is looking for a skilled Machine Learning Engineer to help advance cutting-edge machine learning systems for behavior planning in autonomous driving. You will have the chance to design and implement innovative machine learning models for our next-generation autonomous vehicle platform, influencing millions of Toyota production vehicles. We are looking for individuals who are passionate about self-driving car technology and its potential impact on humanity.

RESPONSIBILITIES

  • Lead the design and development of advanced machine learning models specifically tailored for autonomous vehicles utilizing deep learning, simulation, and large-scale data analysis
  • Deploy scalable and efficient ML models on our autonomous vehicle platform
  • Integrate modern technologies with rigorous safety standards while maintaining cost efficiency
  • Significantly contribute to development of needed components for end-to-end ML training and deployment, from data strategy to optimization and validation
  • Be a champion of the scientific method and critical thinking in inventing state-of-the-art deep learning solutions
  • Work in a high-velocity environment and employ agile development practices
  • Collaborate closely with teams such as Perception, Motion Planning, Simulation, Infrastructure, and Tooling to drive unified solutions
  • Work in a hybrid workspace, with the requirement to be present in our Nihonbashi (Japan) office three days per week

MINIMUM QUALIFICATIONS

  • MS or PhD in Machine Learning, Computer Science, Robotics or related quantitative fields, or equivalent industry experience
  • 3+years of experience with Python, any major deep learning framework, and software engineering best practices
  • Comfortable in writing C++code to help integrate with our autonomous vehicle platform
  • 3+years of experience with deep learning approaches such as pretraining, distillation, unsupervised/supervised learning, multi-task learning, and/or deep reinforcement learning
  • 3+years of experience covering machine learning workflows, data sampling and curation, pre-processing, model training, ablation studies, evaluation, deployment, and inference optimization
  • Strong communication skills with the ability to communicate concepts clearly and precisely

NICE TO HAVES

  • Published research at top-tier conferences (NeurIPs, CVPR and similar)
  • Proven track record of deploying ML models at scale in self-driving or related fields
  • Experience developing large-scale ML models (e.g., LLMs, VLMs, VLAs)
  • Familiarity with production-level coding in time-limited task schedules
  • Experience with computer vision (e.g.multi-view geometry, camera calibration, depth estimation, neural radiance fields, gaussian splatting, simultaneous localization and mapping)
  • Experience with robot motion planning (e.g., trajectory optimization, sampling-based planning, model predictive control)
  • Experience with temporal data and/or sequential modeling
  • Experience in self-driving challenges (Perception, Prediction, Mapping, Localization, Planning, Simulation)
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