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Senior Machine Learning Engineer, Prediction

woven-by-toyota · 30+ days ago
Negotiable
Full-time
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Woven by Toyota is the mobility technology subsidiary of Toyota Motor Corporation. Our mission is to deliver safe, intelligent, human-centered mobility for all. Through our Arene mobility software platform, safety-first automated driving technology and Toyota Woven City — our test course for advanced mobility — we’re bringing greater freedom, safety and happiness to people and society. 

Our unique global culture weaves modern Silicon Valley innovation and time-tested Japanese quality craftsmanship. We leverage these complementary strengths to amplify the capabilities of drivers, foster happiness, and elevate well-being.

TEAM
At Woven by Toyota, we work on a diverse set of problems ranging from solving Trajectory Planning and Decision Making problems, to optimizing latency on hardware accelerators, designing novel neural network architectures and applying and advancing the state-of-the-art of machine learning (ML) for perception, prediction, and motion planning. We are looking for doers and creative problem solvers to join us in improving mobility for everyone with self-driving technology. You will be interacting on a daily basis with other software and hardware engineers and researchers to tackle some of the most challenging problems in AI and Robotics.

WHO WE ARE LOOKING FOR
The team is looking for an experienced Machine Learning Engineer to join us in developing a state-of-the-art motion planning system for autonomous driving. As an expert in machine learning in the Motion Planning team, you will be responsible to design and develop new Machine Learning models for our Motion Planners, and to deploy the model on our next-generation autonomous vehicle platform and ultimately to millions of Toyota production vehicles. The ideal candidate will have published some deep learning research in top-tier conferences such as NeurIPs or CVPR, built and deployed real-world deep learning products, and worked in a fast-paced environment along with other highly talented engineers. We recognize the unique capabilities each team member brings, and encourage applicants to reach out even if they do not match all of the characteristics described below.

RESPONSIBILITIES:

  • Motion Planning ML model R&D by prototyping, validating and iterating on existing and new model architectures leveraging imitation learning, deep reinforcement learning, and large-scale data.
  • Own development of new ML models end-to-end from data strategy, initial development, optimization, production platform validation, and fine tuning based on metrics and on road performance.
  • Lead large, multi-person projects and significantly influence the overall Motion Planning architecture and technical direction.
  • Enable and help other engineers on the team to be more effective through coaching and leading by example when it comes to writing high-quality code, providing high-quality code and design document reviews and delivering rigorous reports from ML experiments.
  • Work in a high-velocity environment and employ agile development practices.
  • Team player and "get things done" mentality
  • Collaborate closely with teams such as Perception, Simulation, Infrastructure, Tooling to drive unified solutions.
  • Closely collaborate with Motion Planning subteams to develop end-to-end solutions.

EXPERIENCE:

  • MS, or higher degree, in Machine Learning, Robotics, CS (or other related fields), or other quantitative fields or equivalent in industry experience.
  • 5+ years of experience with ML frameworks such as PyTorch, Caffe, Tensorflow
  • Extensive experience with learning-based planning approaches like imitation learning, reinforcement learning and state-of-the-art techniques for sequential modeling like Transformer architectures, and camera input or vector/point-based representations.
  • Strong programming skills in Python or C++
  • 5+ years of experience in machine learning workflows: data sampling and curation, pre-processing, model training, ablation studies, evaluation, deployment, inference optimization.
  • Passionate about self driving car technology and its potential for humanity.
  • Strong communication skills and ability to communicate concepts clearly and precisely

Nice to Have

  • Experience with robot motion planning techniques like trajectory optimization, sampling-based planning, model predictive control, etc or self-driving problems (Perception, Prediction, Mapping, Localization, Planning, Simulation)
  • Experience in writing production level code in a real-time operating system.
  • Experience with temporal/sequential modeling and/or reinforcement learning.
  • Experience in optimizing runtime-critical systems for Linux, UNIX-like real-time operating systems on automotive-grade compute platforms, and building safety-critical software architecture.
For California: $161,000- $264,000 a year
Your base salary is one part of your total compensation. AWe offer a base salary, short term and long term incentives, and a comprehensive benefits package. The California pay scale for this full time position is $161,000- $264,000. The total compensation offered to an employee will be dependent upon the individual's skills, experience, qualifications, location, and level.

WHAT WE OFFER
We are committed to creating a modern work environment that supports our employees and their loved ones. We offer many options of the best programs to allow you to do your most meaningful work and to help you shape the future of mobility.
・Excellent health, wellness, dental and vision coverage
・A rewarding 401k program
・Flexible vacation policy
・Family planning and care benefits

Our Commitment
・We are an equal opportunity employer and value diversity.
・Any information we receive from you will be used only in the hiring and onboarding process. Please see our privacy notice for more details.

Our Commitment
・We are an equal opportunity employer and value diversity.
・Any information we receive from you will be used only in the hiring and onboarding process. Please see our privacy notice for more details.

Last updated on Oct 17, 2024

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