- Understand deep-learning fundamentals: architectures, training dynamics, evaluation design, and data quality and annotation.
- Think in systems: latency and memory budgets, failure modes, distributed pipelines, and on-device constraints.
- Take end-to-end ownership across the model, edge, and cloud boundaries, and stay comfortable with ambiguity along the way.
- Thrive in fast-paced environments and can rapidly iterate from experimentation to production.
- Have hands-on experience using agentic development tools and AI-assisted coding as a core part of how you build and ship, not as a side experiment.
- Are proficient in Python and PyTorch, with working ability in at least one systems language (e.g., C++ or Go).
Strong candidates may also have
- Experience building LLM or agent-based applications including tool use, retrieval over domain data, and evaluating non-deterministic systems.
- Experience with on-device or embedded ML (SNPE, TensorRT, TFLite, ONNX Runtime, or similar).
- Experience with video understanding, VLM/embedding models, or large-scale retrieval.
- Experience working with large-scale video or telemetry data, including annotation pipelines and observability tooling (e.g., Snowflake, Redash).
- Experience with distributed training or experimentation frameworks (e.g., Ray/Anyscale) and building rigorous evaluation harnesses.
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