About the role
Bringing the State of the Art to Products Partners with Engineering and Product teams to turn advances in generative AI, search and retrieval, agentic systems, and efficient inference into measurable product impact. Builds prototypes and production-ready platform components for inference, retrieval, and agent workflows across heterogeneous CPUs, GPUs, and NPUs on Windows devices. Develops and evaluates data-, research-, and experimentation-backed techniques for on-device and hybrid inference, including quantization, distillation, model adaptation, compression, indexing, embeddings, and hardware-aware optimization. Investigates intelligent model and workload placement across device and cloud resources while balancing quality, latency, memory, power, reliability, privacy, and cost. Designs datasets, metrics, experiments, and benchmarks for model and system evaluation; analyzes behavior to identify quality and performance bottlenecks and drives improvements across the AI workload lifecycle. Implements and integrates machine learning components, runtimes, developer APIs, and tools, then validates their behavior through production-oriented testing and monitoring on representative hardware and workloads. Builds collaborative relationships across science, engineering, hardware, and product teams; contributes to technical planning and helps teams apply research methods and best practices to platform problems. Bachelor's Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 4+ years related experience (e.g., statistics predictive analytics, research). OR Master's Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 3+ years related experience (e.g., statistics, predictive analytics, research). OR Doctorate in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 1+ year(s) related experience (e.g., statistics, predictive analytics, research). These
requirements include but are not limited to the following specialized security screenings: Master's Degree in Statistics, Mathematics, Physics, Computer Science, Electrical or Computer Engineering, or a related field AND 6+ years of related experience. OR Doctorate in one of these fields AND 2+ years of related experience. OR equivalent experience. 2+ years of experience developing and deploying production machine learning systems. Experience with generative AI, language or multimodal models, agentic systems, model post-training, RAG/Search, Approximate Nearest Neighbor algorithms and ML frameworks.
