Top Machine Learning Engineer Jobs in Seattle, WA

Leads the design, development, and scaling of machine learning systems for signals, attribution, recommendation, ranking, and advertising measurement. Provides technical direction, improves ML infrastructure, collaborates with cross-functional leadership, and promotes scalable, reliable, cost-effective engineering practices. The role requires shipping advanced models, mentoring engineers, solving ambiguous technical problems, and influencing Snap’s broader machine learning strategy.

Design, train, fine-tune, optimize, and deploy large-scale machine learning systems for cloud security use cases. Build end-to-end ML pipelines, develop transformer and embedding models, productionize open-weight language models, and optimize inference for latency, cost, and quality. Architect resilient ML services across AWS and GCP using cloud-native microservices while collaborating with engineering teams on AI strategy and solving complex problems involving massive datasets.

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.

Build, deploy, and maintain machine learning models powering Snapchat products at scale. Own the full ML lifecycle from data analysis through production deployment, apply modern ML techniques, and collaborate with cross-functional teams to launch ML-driven features. Use AI tools to develop scalable services while ensuring code correctness, security, performance, and architectural quality. Mentor collaborators and solve ambiguous, large-scale problems.

Build and deploy machine learning models powering Snapchat products at scale. Own the full ML lifecycle from data analysis through production deployment, apply modern ML techniques, and collaborate with cross-functional teams to launch ML-driven features. Develop scalable, secure, production-ready services using AI-assisted engineering workflows, while contributing to ranking, recommendations, search, content understanding, or image-generation applications.

Build and deploy machine learning models for Snapchat products, contributing across data analysis, training, evaluation, and production deployment. Collaborate with cross-functional teams to launch scalable ML-powered features while maintaining high standards for code quality, security, and reliability. Apply established machine learning techniques to ranking, recommendations, search, content understanding, image generation, and related applications.

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.

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.

Lead machine learning architecture and technical direction across multiple quarters while remaining involved in experimentation, quality, and production systems. Work across model post-training, personalization and recommendation, and agentic experiences. Define solutions for ambiguous problems, align cross-functional partners, drive execution, and safely bring ML systems into production.

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.

Lead the design, development, deployment, and scaling of machine learning models and AI systems powering Atlassian products. Responsibilities include architecture design, experimentation, evaluation, collaboration with product and engineering teams, technical mentorship, and contributing to company-wide AI strategy. The role focuses on search, agentic AI, language models, and intelligent product capabilities.

The Principal Machine Learning Engineer role focuses on developing and leading machine learning engineering initiatives at Atlassian. The provided description emphasizes Atlassian’s flexible work model, compensation, benefits, inclusion commitments, and employment verification requirements, but does not provide detailed technical responsibilities or qualifications.

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.

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.

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.

Design, build, and operate scalable machine learning platform capabilities across feature engineering, model training, inference, monitoring, and lifecycle management. Develop reliable software and APIs, improve production reliability and performance, resolve technical issues, and establish scalable platform architecture. Partner with engineering stakeholders to drive adoption of shared ML infrastructure and safely integrate AI/ML-enabled solutions.

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.

Design, build, and support high-throughput, low-latency LLM inference and data pipelines; collaborate with data scientists to post-train and evaluate models; implement scalable, production-ready systems with strong testing, monitoring, and performance focus; mentor engineers and iterate on architecture, reliability, and user-facing AI applications.

Design and develop machine learning algorithms for target ROAS, automatic bidding, and ads pacing. Explore bid landscapes, optimize advertiser value and revenue, conduct A/B experiments, and collaborate with product, sales, and engineering teams on new ad products and bidding strategies. Provide staff-level technical leadership, drive cross-team alignment, and use AI-assisted development and productivity tools to improve engineering workflows.

Lead design and build of a global AI/ML deployment platform: architect control plane, multi-region deployments, APIs, autoscaling, and integrations. Own technical direction, engage clients, drive governance of AI-assisted development, and ensure resilient, secure production-grade infrastructure.