Top Machine Learning Engineer Jobs in Seattle, WA

Own the strategy, architecture, development, deployment, monitoring, and continuous improvement of production personalization and recommendation systems. Build custom embeddings, ranking models, and shared representations across feeds, search, and recommendations. Lead ambiguous problems from concept to measurable customer impact, establish AI evaluation standards, partner with product and data teams, make cross-team technical decisions, and coach senior engineers.

Own the architecture and evolution of W&B ML workflow systems, including backend services, APIs, SDKs, and frontend components. Design scalable systems, lead live-traffic migrations, resolve complex cross-service issues, reduce technical debt, and improve reliability. Collaborate with product, design, customers, and ML platform teams to deliver workflow solutions. Mentor engineers and establish scalable engineering practices across distributed systems, data platforms, APIs, event-driven infrastructure, or full-stack development.

Lead end-to-end machine learning initiatives for Block’s conversational support systems. Responsibilities include developing and maintaining chatbot and recommendation models, researching LLM, RAG, fine-tuning, and real-time inference architectures, shaping long-term ML roadmaps, and guiding cross-functional teams. The role also requires communicating technical strategy and outcomes to leadership and external stakeholders while delivering scalable, production-ready AI solutions across Cash App, Square, and other Block products.

Leads end-to-end machine learning initiatives for Block’s support experiences, including conversational AI, chatbots, recommendation systems, and agent tools. Defines ML roadmaps, guides research into LLMs, RAG, fine-tuning, and real-time inference, and designs, deploys, and maintains production models. The role requires strategic technical leadership, cross-functional influence, stakeholder communication, and extensive experience delivering applied ML and generative AI systems.

Build and operate large-scale ML infrastructure, including feature pipelines, model training and evaluation systems, high-performance inference services, data management platforms, and model orchestration tools. The role partners with ML engineers to deploy models into production, improve system reliability and efficiency, and maintain secure, scalable, production-ready services using Python, Java, and related AI technologies.

Build and operate large-scale machine learning inference infrastructure for recommendation models, embedding-generation models, and self-hosted LLMs. Responsibilities include model optimization, export, serving, hosting, LLM fine-tuning, reinforcement learning infrastructure, and inference performance improvements. The role supports highly available ML systems across Snap’s advertising, content, growth, camera, and other product teams.

Design, build, deploy, and scale production machine learning systems across data processing, training, evaluation, inference, and operations. Develop reusable infrastructure and services, integrate research innovations into reliable products, drive architecture, and solve scalability, performance, reliability, security, privacy, and Responsible AI challenges. Provide hands-on technical leadership and mentorship across complex engineering initiatives.

Leads technical vision for ads conversion machine learning, developing low-latency deep neural networks and modeling user intent from text, visual, and behavioral signals. The role includes mentoring engineers, guiding applied ML projects, collaborating on ad products, using AI tools to accelerate development, and applying statistical methods and A/B testing to improve model quality and iteration speed.

Lead the technical strategy and development of machine learning solutions for Uber’s homepage personalization, including sequential recommendation, intent-aware ranking, LLM-based systems, and agentic AI. Partner with product teams to improve engagement and business outcomes, influence technical direction across teams, and mentor machine learning engineers.

Leads architecture, development, optimization, and production deployment of machine learning and deep learning models for edge AI systems. Develops computer vision, signal processing, sensor fusion, and distributed ML infrastructure capabilities. Optimizes models for resource-constrained AI chips and edge devices, using Python, C++, PyTorch, and TensorFlow. Provides technical leadership and collaborates with scientists, engineers, and product managers across public safety, enterprise, and defense applications.

Develop and deploy production-grade machine learning systems that generate insights from structured and unstructured financial data. Responsibilities include NLP, summarization, semantic search, classification, prediction, GenAI, and LLM development; model training, monitoring, optimization, and operationalization; scalable data pipeline integration; architectural contributions; and collaboration with engineering, product, and data science teams.

Build and maintain core infrastructure for developing, training, evaluating, deploying, and operating machine learning models and pipelines for Atlassian’s GenAI products. Solve complex software infrastructure and architecture challenges, lead engineers through technical projects from design to launch, and collaborate with internal teams and customers. The role involves large-scale distributed systems, data processing, cloud platforms, CI/CD, and machine learning lifecycle support.

Develop and deploy machine learning models and data pipelines for automated decision-making. Responsibilities include collecting and transforming data, exploratory analysis, predictive modeling, production deployment, model serving infrastructure, monitoring drift and performance, and communicating insights to stakeholders. The role requires strong Python and SQL skills, experience with cloud platforms and MLOps tooling, and collaboration across global teams.

Design and implement scalable data pipelines and ML algorithms to optimize StackAdapt's digital advertising platform. Collaborate with teams to solve complex problems.

Design, develop, and productionize machine learning models and pricing algorithms for Uber’s large-scale, real-time marketplace systems. The role focuses on deep learning, causal modeling, reinforcement learning, optimization, and online serving at high volume. Responsibilities include collaborating with engineers, product managers, and scientists, shaping technical direction, solving complex marketplace problems, mentoring junior engineers, and monitoring ML solutions across offline and online environments.

Design, train, and validate machine learning models that improve simulated LiDAR, radar, and camera realism for autonomous vehicle simulation. Build datasets from real and simulated sensor data, integrate 3DGS and NeRF reconstruction, develop generative augmentation methods, define sensor realism metrics, reduce sim-to-real gaps, and mentor engineers and interns.

Develop and validate machine learning models that improve simulated LiDAR, radar, and camera realism for autonomous vehicle simulation. Build datasets from real and simulated sensor data, integrate 3DGS and NeRF reconstruction, apply generative augmentation, define sensor realism metrics, close sim-to-real gaps, and mentor engineers and interns.

Lead the technical strategy, architecture, development, and productionization of large-scale machine learning and pricing systems for Uber’s real-time marketplace. Develop deep learning, causal, reinforcement learning, optimization, and algorithmic pricing solutions serving billions of trips. Mentor engineers, influence cross-team technical direction, communicate with executives, and deliver high-impact systems for millions of drivers.

Develop, implement, and deploy scalable machine learning models for healthcare solutions. Collaborate cross-functionally to integrate models, analyze data to improve performance, ensure data quality, and stay current with ML advancements to support patient care and operational efficiency.

Build and deploy advanced machine learning and agentic AI systems that improve Airbnb’s customer service experience. Responsibilities include developing AI assistants, integrating ML products, optimizing model performance, applying LLM techniques such as fine-tuning and RAG, building multi-agent architectures, and shipping scalable production ML systems. The role collaborates closely with Engineering, Product, and Design teams and contributes to emerging AI initiatives from concept through production.

Own the end-to-end computer vision platform for fine-grained item identification and pricing. Responsibilities include designing and fine-tuning vision models, defining evaluation and confidence standards, building labeling feedback loops, prioritizing coverage versus accuracy, and deploying reliable, low-latency serving infrastructure at scale. The role also communicates model capabilities and trade-offs to technical and non-technical stakeholders.

Lead technical direction for Pinterest’s foundational machine learning capabilities in visual representation learning, multimodal content understanding, and LLM-based personalization. Drive initiatives from research and experimentation through productionization and adoption. Build reusable embeddings, models, semantic signals, and ML infrastructure using distributed training and data pipelines. Collaborate across product, engineering, data science, and applied science while mentoring technical teams and guiding multi-quarter roadmaps.

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.

Design, develop, and implement AI/ML models; analyze large datasets; integrate AI solutions into existing systems; apply MavLink for autonomous systems; collaborate across teams and improve ML development processes.

Design, build, and deploy autonomous AI agents using LLMs and agent frameworks; integrate agents into e-commerce and supply chain systems; implement multi-agent orchestration, memory and tool integrations; fine-tune models and apply RL concepts; and build safety, monitoring, and MLOps pipelines.