Senior AI/ML Engineer

About the Role

Requirements:

• We are deliberately strict on a small number of things and flexible on everything else.
• A working command of numerical analysis and matrix theory. Jacobian/Hessian estimation, spectral properties, conditioning, and floating-point error analysis should be tools you reach for by reflex, not topics you once studied. You should be able to say why an estimator's variance blows up, and when that matters.
• A real mental model of neural network training. Loss landscapes, gradient and error propagation, optimizer dynamics, and the mechanics by which training goes wrong. You should have opinions about what a gradient norm spike does and does not tell you.
• An adversarial instinct for numerical edge cases. Typical inputs rarely find anything. We are looking for someone who reaches for denormals, extreme dynamic range, catastrophic cancellation, degenerate shapes, and accumulation-order effects — someone whose first question about a passing test is what that test failed to exercise.
• The engineering to make the math run. High proficiency in PyTorch and Python, and a track record of building analytical tools that are both mathematically defensible and fast enough to be used in production training and evaluation loops.
• The judgment to make analysis actionable. Much of this role is turning a numerical result into something an engineer who will not read your derivation can act on: knowing which quantity actually answers the question being asked, tracing an anomalous number back to the operation that produced it, etc
• Bachelor's, Master's, or PhD in Applied Mathematics, Control, Physics, Computer Science, Data Science, or a closely related quantitative field.
• What You'll Learn Here
• You are not expected to arrive with these. They are the parts of the job that are genuinely specific to this environment, and we expect them to take three to nine months:
• The training recipes, the acceleration techniques in our stack
• The semantics of autonomous driving behavioral metrics and what actually constitutes meaningful behavioral drift for a vehicle.
• Model compression portfolio, the deployment path to the car, and the organizational context around a ship decision.
• Our parity validation workflow and where the quantization and compilation toolchains hide their sharp edges.
• What Will Give You a Competitive Edge
• Hands-on experience debugging large-scale training runs: diagnosing loss spikes, resolving numerical divergence, and running deep investigations into training dynamics.
• Experience with distributed training beyond DDP — FSDP, Megatron-LM, DeepSpeed, 3D parallelism — particularly designing low-overhead observability over sharded parameter and gradient state.