Top Machine Learning Jobs in Seattle, WA

Lead the architecture, development, deployment, monitoring, and operational excellence of large-scale AI observability services and platforms. Drive scalable, reliable, secure distributed systems; make architecture and dependency decisions; collaborate with product and engineering teams; engage customers; improve automation and deployment safety; mentor engineers; and champion testing, privacy, compliance, and code quality.

Lead the architecture, development, deployment, and operation of scalable AI services and observability platforms. Drive technical strategy, reliability, security, performance, automation, and operational excellence. Partner with product and engineering teams, engage customers, resolve complex issues, make architecture decisions, manage dependencies, mentor engineers, and promote high-quality engineering practices across testing, privacy, compliance, and code quality.

Design, implement, and operate high-performance, low-latency distributed ad serving systems hosting machine learning models. Lead feature design, code review, performance profiling, capacity planning, automation, and on-call operations to meet SLA, security, and reliability goals.

Lead development of auction and bidding algorithms for Microsoft Advertising: design and implement bidding strategies and auction mechanisms, run large-scale experiments, build automation and marketplace optimization systems, and develop causal and ML models to improve advertiser ROI and long-term marketplace health.

Leads and develops an applied science team building AI-powered Excel experiences. Sets scientific and technical direction across machine learning, LLMs, retrieval, agentic systems, evaluation, and production deployment. Translates customer needs and system failure modes into research and product priorities, drives experimentation and releases, establishes quality and safety standards, manages datasets and evaluation frameworks, and partners with engineering, product, customers, and Microsoft AI communities.

AI software engineering intern in Boeing’s Graduate Researcher Program, developing and evaluating learned models and contributing to machine learning applications for aerospace. The role involves preparing data, supporting machine learning development and deployment pipelines, and collaborating with distributed engineering teams. Candidates must be PhD students who have completed their first year by July 2027 and can work onsite full-time for 10–12 weeks during Summer 2027.

Conduct an independent data science project from problem definition through analysis, experimentation, validation, and recommendations. Analyze large-scale behavioral, commerce, content, and advertising data; apply statistics, causal inference, machine learning, and AI evaluation methods; develop metrics and monitoring; and collaborate with product, engineering, economics, and business teams. Communicate findings clearly, document reproducible work, and influence product decisions.

Own and build the company’s machine learning infrastructure, including a shared feature store, model-serving systems, drift monitoring, and unified deployment pipelines. Establish long-term MLOps standards and processes from the ground up, while collaborating with data scientists and engineers to drive adoption and improve production model reliability.

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.

Defines architecture and technical strategy for evaluating LLM, RAG, and agentic AI systems across the product lifecycle. Designs evaluation frameworks, metrics, datasets, dashboards, release gates, monitoring, and experimentation workflows. Analyzes model behavior, retrieval quality, failure modes, safety, and product impact, then guides improvements across prompts, tools, orchestration, search, UX, and code. Partners with engineering, applied science, data science, and product teams; leads cross-functional initiatives and mentors technical staff.

Develop and maintain production machine learning models, build scalable training and serving technologies, optimize real-time and batch ML pipelines, and monitor model performance and drift. The role involves deep neural networks, recommendation systems, automated retraining, ML research, engineering excellence, and communicating technical concepts to diverse audiences. Candidates need 6+ years of industry ML experience, strong coding skills, and experience deploying neural networks at scale.

Own the architecture and technical direction for Atlassian’s enterprise agentic search platform. Design retrieval, agent orchestration, context construction, evaluation, LLM training, serving, and continuous improvement systems. Address enterprise permissions, tenant isolation, freshness, scalability, latency, cost, privacy, and reliability. Validate architecture through prototypes and production implementations, establish benchmarks and feedback loops, guide cross-team roadmaps, influence technical adoption, and mentor senior engineers and scientists.

Lead the design and delivery of knowledge graph and machine learning systems that infer personal work context from connected tools. Build graph inference pipelines, schemas, permission-aware APIs, Rovo Chat integrations, and MCP-compatible CLI experiences. Establish evaluation frameworks, improve retrieval and grounding, provide technical direction across teams, mentor engineers, and ensure responsible, privacy-safe AI systems operate reliably at production scale.

Leads and grows Applied Science teams developing text, image, and multimodal AI for advertising creatives. Defines science roadmaps for generation, personalization, understanding, evaluation, and optimization; drives LLM and generative AI innovation into production; establishes experimentation and impact metrics; and partners with Engineering, Product, Design, and TPM teams to deliver scalable advertiser-facing solutions. The role requires technical leadership, people management, strategic execution, and communication with senior stakeholders.

Design and optimize large-scale machine learning model-serving systems, including distributed infrastructure, load balancing, auto-scaling, batching, caching, and GPU inference optimization. Build reliable, high-concurrency services handling billions of requests, benchmark and accelerate inference engines, develop CI/CD for model deployment, and partner with ML engineers to fine-tune and deploy open-source large language models.

Provides technical leadership for document verification machine learning solutions. Oversees the full ML lifecycle, including data strategy, model development, evaluation, deployment, monitoring, and improvement. Mentors data scientists, establishes technical standards, identifies risks, and improves model performance, scalability, and reliability. Partners with engineering, product, and customers to translate needs into modeling strategies and communicates technical decisions to diverse stakeholders.

Build AI agent harnesses, research workflows, evaluations, and integrations that enable Claude to conduct science and engineering research. Connect simulation tools, define verifiable tasks, prototype with expert partners, deploy pilots, and convert successful engagements into reusable systems. Collaborate with research and product teams while communicating model capabilities, limitations, and tradeoffs to technical and nontechnical stakeholders.

Provides technical and strategic counsel to the Microsoft AI CEO on high-stakes research decisions. The role evaluates models, prototypes solutions, analyzes technical tradeoffs, and supports work spanning pre-training, post-training, multimodal systems, infrastructure, safety, and alignment. The advisor synthesizes technical findings into executive recommendations, drives initiatives from conception through execution, tracks emerging AI research, and facilitates communication between leadership and research teams.

General interest posting for candidates who attended COLM 2026 and connected with Scale AI. The company is seeking potential candidates passionate about machine learning, AI research, generative AI, defense applications, and autonomous vehicles for future opportunities across multiple roles.

Design, build, and scale cloud-native distributed systems and runtimes to deploy, run, fine-tune, and observe enterprise AI agents. Drive architecture, optimize model/infrastructure interactions, improve developer iteration tooling, enforce secure-by-design practices, and mentor engineering teams to deliver production-ready agent platforms.

Designs, develops, and troubleshoots secure, scalable ML software systems and services. Builds production code, architecture artifacts, APIs, and distributed microservices; analyzes data to improve applications and system architecture. Uses AI-assisted development tools responsibly, validates generated outputs, and guides peers on secure engineering practices. Requires Python and ML framework expertise, cloud-native technologies, public cloud experience, distributed systems knowledge, and Cassandra or equivalent NoSQL experience. Preferred experience includes GPU workloads, model serving, distributed inference, model compression, and edge deployment.

Lead development and operation of scalable machine learning training platforms across AWS and Kubernetes. Optimize distributed GPU workloads, training performance, reproducibility, cost, and observability. Build CI/CD automation, standardized containers, governance, and self-service workflows for deep learning and generative AI training. Partner with platform and data engineering teams on security, access, and infrastructure standards. Lead responsible adoption of AI-assisted development tools and coach engineers on secure, compliant validation practices.

Designs, develops, and troubleshoots secure, scalable ML systems and services. Builds production software in Python using ML frameworks, cloud-native technologies, distributed systems, microservices, and NoSQL databases. Creates architecture artifacts, analyzes large datasets, improves system reliability and performance, and uses AI-assisted development tools responsibly. Preferred experience includes GPU workloads in Kubernetes, model-serving frameworks, distributed inference, model compression, and edge deployment.

Build and operate scalable machine learning training platforms and pipelines across AWS and other cloud environments. Optimize GPU-based single-node and distributed training for performance, cost, stability, and reproducibility. Develop Kubernetes infrastructure, GenAI and LLM fine-tuning workflows, observability, CI/CD automation, governance controls, and developer self-service tools. Partner with data engineering and platform teams while applying secure, responsible AI-assisted development practices.

Lead design, build, and operate scalable ML training platform on cloud. Optimize GPU single-node and distributed training, manage Kubernetes-based infrastructure, enable GenAI/LLM fine-tuning workflows, implement observability, improve developer CI/CD and automation, and enforce governance, security, and responsible AI practices.