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

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