SDE IV (ML Engineering)

Turn the Data Scientist's findings into production systems: finetuning/distillation pipelines, routing algorithms, cost-optimization logic.

Build data pipelines and platform components as owned products with real interfaces, not one-off scripts.

Own the engineering rigor of shared evaluation infrastructure so both model work and algorithm work rely on the same trusted foundation.

Ship, monitor, and iterate on what's in production, closing the loop back to the Data Scientist on what's working.

Strong software/ML engineering fundamentals — production code quality, comfort building and shipping pipelines and services.

Genuine willingness to learn model finetuning and distillation on the job — prior experience is a plus, not a requirement.

Algorithmic thinking for routing/optimization problems under cost, latency, and non-determinism constraints.

Comfortable working from someone else's analysis rather than always originating the “what should we build” question.