Staff, ML Engineer - Scene Generation
Job description
About the role
Your work will close the domain data gap to advance safe autonomous driving.
Software engineers turn product ideas into working code. Engineers work in small teams, review each other's work, and ship in small batches. Most teams follow agile practices such as sprints and daily standups. Engineers also write tests, fix bugs, and improve performance. The field values clear communication as much as technical skill. Engineers spend part of every week on planning, code review, and debugging, not just writing new code. The ability to explain a technical decision in plain words separates strong engineers from the rest.
Key facts
What you'll do
Technical leadership sets the design decisions and drives consensus across engineers and stakeholders for the Scene Generation team.
Day-to-day leadership of the Scene Generation team prioritizes work, unblocks engineers, and makes final architectural and technical decisions.
The latest research in Neural Rendering and generative models is applied and guided to advance the team's capabilities.
Production quality software is designed, implemented, tested, and deployed using disciplined software development processes to set the team's technical bar.
Requirements
The posting states a minimum of 7 years of experience.
A Bachelor's, Master's, or PhD degree in a relevant technical field is required alongside demonstrated competences typically acquired through 10+, 7+, 5+, or 8+ years of experience.
Proficiency in Python and deep learning frameworks such as PyTorch is required.
A PhD or equivalent work experience of 8+ years in a relevant field is required, with industry experience shipping production software and leading technical teams.
Expertise in Neural Rendering, including Neural Radiance Fields and 3D Gaussian Splatting, is required.
Generative models expertise, including Diffusion Models and Flow Matching, is required.
Experience with production data in robotics and/or autonomous driving is required.
Background in Computer Vision, Computer Graphics, 3D Reconstruction, or 3D Computer Vision is required.
Handling of autonomous driving sensor data across multiple timestamps and sensor modalities, including cameras, LiDAR, and radar, is required.
Expertise is recognized for conducting complex, high impact work with minimal supervision and wide latitude for independent judgment.
Experience with VDI and cloud-based machine learning development environments is required.
Team leadership experience is required, including setting direction and driving consensus across engineers and stakeholders.
Mentoring and developing engineers, including more senior individual contributors, is required.
Ability to design, maintain, and own team technical solutions and drive alignment across team interfaces to the rest of the organization is required.
Nice to have
Experience with autonomous driving or robotics perception in production environments is a bonus.
Proficiency with CUDA programming for efficient rendering of large-scale scenes is a bonus.
Publications in top tier CV, AI, or Graphics conferences or journals are a bonus.
Experience with MLOps and infrastructure tools such as Ray is a bonus.
Familiarity with 3D labeling, calibration, and sensor simulation pipelines is a bonus.
Prior experience as a formal people manager or tech lead is a bonus.
Practical notes
This role operates in a Remote
US, Ann Arbor, MI arrangement.
Typical interview steps
Hiring for engineering roles usually starts with a recruiter screen, followed by one or two technical rounds. Candidates often solve a coding problem, discuss past projects, and answer system design questions. Some loops include a take-home task. Final rounds typically cover team fit and give candidates a chance to ask questions. Interviewers look for how you break down an unfamiliar problem, not just whether you reach the answer. Practicing a few problems aloud and reviewing your own past projects are the best preparation.