Senior Staff Machine Learning Software Engineer

We are building a product where learned models and compute-heavy inference components have to run inside a tight local runtime budget.

Research code is only the starting point.

This role owns the path from a working prototype to production inference that is measured, packaged, tested, and ready for repeated use in the field.

You will work closely with the people developing the underlying algorithms, but your ownership is different: production readiness, performance, reliability, and the engineering boundary between exploratory model work and shipped execution.

The strongest fit is someone who can explain the bottleneck they found, the number they moved, the tradeoff they accepted, and the test that kept the fix from regressing.

If your best work is making inference faster, smaller, more predictable, and easier to ship, this role is likely a good match.

• Turning research prototypes into production inference components with explicit latency, throughput, memory, and accuracy budgets
• Optimizing the execution path: tensor layout, host/device transfers, batching strategy, kernel launch overhead, mixed precision, quantization, and memory reuse
• Writing or tuning Rust, C++, and CUDA where framework-level optimization is not enough, then validating the improvement with profiler output and release-facing tests
• Building inference-adjacent evaluation machinery: calibration checks, confidence behavior, regression detection, dataset slices, and failure-mode reporting tied to product metrics
• Maintaining the deployment contract: model artifacts, runtime integration, versioning, reproducibility, and performance gates that block unsafe changes
• Algorithm research and novel model design live on a separate track. You will collaborate with that team, translate prototypes into production constraints, and surface shipping risks early when a design needs to change.