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Responsibilities:
- Build and operate the ML infrastructure and platforms powering AI products.
- Design systems for model training, evaluation, deployment, inference, and experimentation.
- Build and optimize model serving and inference infrastructure for high-throughput and low-latency workloads.
Outcomes:
- AI infrastructure reliably supports production workloads at scale.
- Models can be trained, evaluated, deployed, and improved efficiently.
- Inference systems deliver strong latency, throughput, reliability, and cost efficiency.
Qualifications:
- ML and AI experience are required.
- Strong software engineering fundamentals and experience building production systems.
- Experience building ML infrastructure, platforms, or production ML systems.