Own the research-to-production pipeline: turn checkpoints into production models.
Partner with researchers to productionize models with robust workflows.
Build tooling for training, evaluation, packaging, and deployment.
Design and own model release gates with automated checks.
Optimize production inference: latency, batching, memory, throughput.
Strengthen build/delivery across GPU compute and cloud for fast shipping.

🎯 Requirements

Strong software engineering fundamentals with Python.
Experience taking ML models from research to production at scale.
Working understanding of modern DL stack (PyTorch) for training/evaluating/serving.
Experience building ML pipelines: orchestration, evaluation, packaging, deployment, CI/CD.
Familiar with serving and inference optimization: latency, throughput, batching.
Comfort operating across distributed systems and GPU compute (cloud/on-prem).

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