Top AI & Machine Learning Jobs in Seattle, WA

Build SDKs, frameworks, reference applications, and evaluation tools for Physical AI systems operating across cloud, edge, and real-world hardware. Integrate AI models, inference runtimes, agent frameworks, and connected devices. Maintain development hardware fleets, improve platform security and reliability, document open-source tools, and collaborate with engineering, product, architecture, and industry partners.

Design and build MLOps infrastructure for production deployment of data, machine-learning models, and software. Develop end-to-end ML pipelines, CI/CD/CT workflows, secure repository strategies, containerized deployments, and automation for Edge AI applications. Collaborate with developers, data scientists, and ML engineers to define tooling, monitor DevOps performance, and improve operational efficiency. Support embedded-system CI/CT, open-source pipelines, and model testing.

Lead research and development of perception algorithms for Physical AI, including detection, segmentation, tracking, localization, sensor fusion, and scene understanding. Define rigorous datasets, experiments, metrics, and evaluation pipelines, then optimize models for real-time deployment across Arm CPUs and AI accelerators. Collaborate with robotics, controls, hardware, product, and customer teams; mentor technical staff; guide on-device perception roadmaps; and contribute through publications, patents, and open-source work.

Lead the architecture and hands-on development of distributed AI inference runtime systems. Responsibilities include scheduling, batching, KV-cache and memory management, distributed execution, kernel optimization, profiling, benchmarking, and performance analysis. Enable new model architectures, improve latency and throughput, develop validation and safe-rollout systems, and collaborate with infrastructure, compiler, hardware, cloud, and research teams. Mentor engineers and establish performance-engineering practices for Arm’s production AI platform.

Leads architecture and hands-on development of distributed AI inference runtime capabilities, including scheduling, batching, KV-cache management, memory allocation, kernel optimization, model execution, and performance benchmarking. Partners with infrastructure, compiler, hardware, cloud, and research teams to improve latency, throughput, reliability, and resource efficiency. Provides technical leadership, conducts reviews, mentors engineers, and establishes performance-engineering practices for production AI workloads.

Build foundational platform software, runtime infrastructure, APIs, SDKs, and developer tooling for on-device physical AI workloads. Develop reliable deployment, lifecycle management, observability, and diagnostics systems under edge constraints. Optimize performance across CPUs and AI accelerators while creating stable abstractions over AI frameworks, models, and hardware. Collaborate with architects, product managers, and AI engineers on production-ready platform capabilities, reference implementations, documentation, and workflows.

Design and implement scalable platform services, runtimes, and developer tooling for deploying and managing AI workloads on Arm-based edge devices. Build abstractions, observability, and performance optimizations across heterogeneous hardware while collaborating with architects and AI engineers to produce robust, maintainable systems and SDKs for on-device AI.