Machine Learning Engineer

Highlights

• Own perception models end-to-end, from data to on-vehicle deployment.
• Build evaluation that predicts on-road behavior beyond offline metrics.
• Trade accuracy against latency and memory on fixed vehicle compute.

Ideal candidate

An experienced ML engineer who has shipped models to real hardware and owns problems end-to-end. You go deep in one domain, question metrics honestly, and defend hard trade-offs with cross-functional partners.

Requirements

• Shipped a neural network to production or real hardware with 3+ years end-to-end experience
• Deep expertise in computer vision, large language models, or generative modelling
• Fluent in Python and a modern deep learning framework
• Enough C++ to read vehicle inference code and collaborate with owners
• Comfortable with large-scale data tooling and SQL to self-serve data
• Can critically evaluate papers and explain technical trade-offs clearly