A visionary Machine Learning Engineer to join our founding team who will help bridge the gap between high-level AI research and real-world physical actuation for our next-generation autonomous transport platforms. We are actively hiring across three core specialized subcategories: AI Research, Post-Training Optimization, and Data Engineering.

Research and develop cutting edge RL and distillation techniques for trajectory planning

Integrate emerging research from the broader AI community, identifying and prototyping the most promising solutions

Design and deploy end-to-end multimodal models that translate real-time visual perception and high-level behavioral goals into physical vehicle actuation

Develop interactive world models from raw multi-sensor logs, allowing the team to re-simulate events and query what a vehicle would see if it altered its trajectory

Ensure core autonomous driving models can seamlessly adapt to novel urban environments and edge cases

Partner with validation and QA teams to run model releases through rigorous simulated scenarios, detecting regressions and identifying systemic performance bottlenecks.

Own the post-training lifecycle by distilling, quantizing, and optimizing massive models to run with low latency on vehicle edge hardware.

Profile real-time inference pipelines to identify and eliminate CPU, GPU, and memory bandwidth bottlenecks on the vehicle.

Work with low-level hardware, electrical, and firmware teams to iterate on custom carrier boards, sensor interfaces, and GPUs on edge devices.

Benchmark and deploy models utilizing hardware-accelerated runtimes (e.g., TensorRT, CUDA) to minimize inference times under strict constraints.

Architect automated pipelines to ingest, filter, and identify rare, high-value, and long-tail scenarios out of multi-petabyte multi-sensor datasets.

Staff Machine Learning Infrastructure Engineer

Responsibilities

Qualification

Required

Benefits

Company

Cssmerge

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