Develop deep learning models for autonomous vehicle trajectories using imitation and reinforcement learning. Collaborate with cross-functional teams to enhance vehicle behavior.
The Prediction & Behavior ML team is responsible for developing machine learning (ML) algorithms that learn and predict behaviors from data, applying them both on-vehicle to influence driving behavior and off-vehicle to provide ML capabilities to simulation and validation. Given the tight integration of behavior forecasting and motion planning, our team collaborates closely with the Planner team to advance overall vehicle behavior. We also work closely with our Perception, Simulation, and Systems Engineering teams to accelerate our ability to validate our driving performance.
As a Learned Trajectory Machine Learning Engineer you will be responsible for developing deep learned models that produce trajectories for our vehicles to drive. Given the tight integration of behavior prediction and motion planning, you will closely collaborate with the Planner and Perception teams in the advancement of our overall vehicle behavior.
In this role, you will:
- You will develop new deep learning models that use imitation learning and reinforcement learning to generate driving plans for our autonomous vehicle. You will also work on techniques to estimate the quality of those driving plans along the dimensions of safety, progress, comfort etc.
- You will leverage our large-scale machine learning infrastructure to discover new solutions and push the boundaries of the field
- You will develop metrics and tools to analyze errors and understand improvements of our systems
- You will collaborate with engineers on Perception, Planning, and Simulation to solve the overall Autonomous Driving problem in complex urban environments
Qualifications
- BS, MS, or PhD degree in computer science or related field
- Experience with training and deploying transformer-based model architectures and reinforcement learning
- Experience with production Machine Learning pipelines: dataset creation, training frameworks, metrics pipelines
- Fluency in C++ or Fluency in Python with a basic understanding of C++
- Extensive experience with programming and algorithm design
- Strong mathematics skills
Bonus Qualifications
- Conference or Journal publications in Machine Learning or Robotics related venues
- Prior experience with Prediction and/or autonomous vehicles or robotics in general
About Zoox
Zoox is developing the first ground-up, fully autonomous vehicle fleet and the supporting ecosystem required to bring this technology to market. Sitting at the intersection of robotics, machine learning, and design, Zoox aims to provide the next generation of mobility-as-a-service in urban environments. We’re looking for top talent that shares our passion and wants to be part of a fast-moving and highly execution-oriented team.
Follow us on LinkedIn
Accommodations
If you need an accommodation to participate in the application or interview process please reach out to [email protected] or your assigned recruiter.
A Final Note:
You do not need to match every listed expectation to apply for this position. Here at Zoox, we know that diverse perspectives foster the innovation we need to be successful, and we are committed to building a team that encompasses a variety of backgrounds, experiences, and skills.
Top Skills
C++
Deep Learning
Machine Learning
Python
Reinforcement Learning
Zoox Foster City, California, USA Office
4000 E 3rd Ave, Foster City, CA, United States, 94404
Zoox Foster City, California, USA Office
1149 Chess Drive, Foster City, CA, United States, 94404
Zoox Fremont, California, USA Office
47540 Kato Road, Fremont, CA, United States, 94538
Zoox San Francisco, California, USA Office
60 Broadway St, San Francisco, CA, United States, 94111
Similar Jobs
Artificial Intelligence • Machine Learning • Robotics • Software • Transportation • Design • Manufacturing
Develop deep learned models and collaborate on generating driving plans for autonomous vehicles, employing imitation and reinforcement learning techniques.
Top Skills:
C++Deep LearningMachine LearningPythonReinforcement LearningTransformer-Based Models
Aerospace • Artificial Intelligence • Hardware • Robotics • Security • Software • Defense
As a Research Engineer in Machine Learning at Anduril, you will optimize ML algorithms for edge devices, prototype LLM-based systems, and benchmark models while collaborating across business lines to identify new research problems.
Top Skills:
Deep Learning ModelsMl AlgorithmsPythonPyTorchTransformer Architectures
Cloud • Information Technology • Machine Learning
The IT SOX Director leads the company's IT SOX compliance program, focusing on IT General Controls and application controls, ensuring compliance and collaborating with various teams.
Top Skills:
CoupaGitIt General Controls (Itgcs)NetSuiteSalesforceWorkday
What you need to know about the San Francisco Tech Scene
San Francisco and the surrounding Bay Area attracts more startup funding than any other region in the world. Home to Stanford University and UC Berkeley, leading VC firms and several of the world’s most valuable companies, the Bay Area is the place to go for anyone looking to make it big in the tech industry. That said, San Francisco has a lot to offer beyond technology thanks to a thriving art and music scene, excellent food and a short drive to several of the country’s most beautiful recreational areas.
Key Facts About San Francisco Tech
- Number of Tech Workers: 365,500; 13.9% of overall workforce (2024 CompTIA survey)
- Major Tech Employers: Google, Apple, Salesforce, Meta
- Key Industries: Artificial intelligence, cloud computing, fintech, consumer technology, software
- Funding Landscape: $50.5 billion in venture capital funding in 2024 (Pitchbook)
- Notable Investors: Sequoia Capital, Andreessen Horowitz, Bessemer Venture Partners, Greylock Partners, Khosla Ventures, Kleiner Perkins
- Research Centers and Universities: Stanford University; University of California, Berkeley; University of San Francisco; Santa Clara University; Ames Research Center; Center for AI Safety; California Institute for Regenerative Medicine
