10 days ago
Base Salary
$208k - $264k/yr
Responsibilities
- Research and develop reinforcement learning and distillation techniques for trajectory planning.
- Design and deploy multimodal models and interactive world models for autonomous vehicle perception, behavior, and actuation.
- Adapt autonomous driving models to novel urban environments and edge cases while partnering with validation and QA teams on simulated releases.
- Distill, quantize, profile, benchmark, and deploy models for low-latency inference on vehicle edge hardware.
- Collaborate with hardware, electrical, and firmware teams on carrier boards, sensor interfaces, and edge GPUs.
- Build automated pipelines to ingest, filter, and extract rare scenarios from multi-petabyte multi-sensor driving datasets.
- Implement data curation, active learning, and statistical quality metrics for model training and simulation testing.
Requirements
- 4+ years of non-internship professional machine learning engineering experience.
- Deep expertise applying AI Transformers to robotics, physical actuation, or spatiotemporal data.
- Experience designing or training multimodal systems, large-scale VLA models, or generative diffusion models.
- Strong sensor-fusion experience with Cameras, LiDAR, and Radar.
- Fluency in PyTorch or JAX and strong programming skills in Python and C++.
- Experience with model optimization, distillation, deployment, edge hardware, profiling, or runtime compilation.
- Experience building data-curation pipelines, active-learning workflows, or data-mining architectures for large physical datasets.
- Familiarity with multi-task learning, Birds-Eye-View frameworks, representation learning, data tokenization, robotics data structures, and spatial frameworks is preferred.
Benefits
- Medical, dental, vision, disability, and life insurance.
- Flexible Spending Account and Health Savings Account options.
- 401(k), equity eligibility, sick time, unlimited flexible time off, and paid holidays.
- Paid parental leave and a pre-tax commuter benefit plan.
- Team lunch in the SoMa office every Tuesday and Thursday.
- Based in San Francisco and onsite five days per week for office-based teams.
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