Top Senior Machine Learning Jobs in Seattle, WA

Design and develop high-scale distributed AI infrastructure services supporting model training and inference on Azure Machine Learning. Build backend services using C#, Go, Rust, and C++ on Kubernetes and Docker. Operate reliable, secure, low-latency systems with 24/7 availability, provide operational ownership, collaborate with engineers and researchers, and provide technical leadership.

Build and deploy production machine learning systems for personalized recommendations, fraud detection, risk scoring, ranking, classification, and anomaly detection. Own the full ML lifecycle, including mathematical formulation, feature engineering, training, evaluation, A/B testing, deployment, monitoring, and reliability. Develop low-latency real-time inference services using large-scale donor and transaction data, collaborate with Platform and DevOps teams, and mentor engineers on ML best practices and scientific rigor.

Develop and operate production ML and AI systems powering patient experiences such as matching, ranking, recommendations, onboarding, and personalization. Build reusable ML infrastructure, pipelines, evaluation, observability, and model-serving capabilities. Provide technical leadership across the ML lifecycle, guide architecture, evaluate custom versus foundation-model approaches, and partner with ML and Patient Engineering teams to deliver reliable, scalable AI products.

Own and advance Wonderlic’s AI-powered Jobs Engine, using production machine learning and NLP to analyze labor-market data and infer job characteristics at scale. Partner with product, engineering, and I-O psychology teams to integrate responsible AI into assessment and workforce solutions. Apply fairness, explainability, legal defensibility, and rigorous evaluation practices to employment-related AI systems, including generative AI outputs and assessment interpretation.

Leads the AI Engineering team across applied AI features and foundational ML infrastructure. Responsibilities include managing Senior and Staff engineers, hiring and coaching, deploying LLM-powered capabilities, building MLOps and AI observability platforms, balancing product delivery with platform investment, and ensuring privacy, compliance, evaluation rigor, and clinical safety in production systems.

Build and operate large-scale machine learning systems for ads personalization, including ad ranking, conversion-rate prediction, recommendation, behavioral modeling, and bidding optimization. Monitor model quality, calibrate predictions, address data drift and delayed feedback, and guide technical direction and project execution. The role requires collaboration with engineering leads and communication of complex technical concepts to non-engineering stakeholders.

Leads the design, development, deployment, and measurement of large-scale machine learning, information retrieval, LLM, and agentic AI solutions. Defines scientific strategy, technical roadmaps, experimentation frameworks, and evaluation methods for web content quality, trust, safety, and understanding. Partners across engineering, product, and research teams, mentors scientists, influences organizational direction, and develops systems addressing spam, abuse, fraud, and low-quality AI-generated content.

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.

Lead the design, productionization, and operation of machine learning and agentic AI capabilities. Partner with product engineering, shape AI/ML architecture, build evaluation systems and data pipelines, and develop reliable MLOps tooling. Champion responsible AI, safety, latency, cost, and error handling. Serve as a technical resource for engineers, influence direction, mentor others, and occasionally explain AI functionality to customers or customer-facing teams.

Designs, trains, evaluates, and operationalizes intelligent document-processing solutions using OCR, machine learning, LLMs, Python, and cloud data pipelines. Builds extraction, validation, reconciliation, exception-handling, and human-review workflows for sensitive information. Develops secure, auditable, configuration-driven systems with API integrations, monitoring, testing, CI/CD, and infrastructure as code. Partners with business experts to define document fields, quality thresholds, validation rules, and acceptance criteria.

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.

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.

Build, train, deploy, and optimize production-scale machine learning models and neural networks. Design scalable data pipelines, distributed training systems, backend improvements, and high-throughput inference solutions. Conduct metric-driven research, improve model accuracy and performance, maintain secure and high-quality code, mentor junior engineers, and collaborate cross-functionally. The role focuses on deep learning, computer vision or NLP, and taking ML products from concept through production.

Develop and deploy production-scale machine learning models, from data gathering and neural network design through training, optimization, scalable deployment, and continuous performance improvement. Build highly available distributed systems and backend APIs, improve engineering standards and tooling, conduct metric-driven research, mentor engineers, collaborate cross-functionally, and help define product strategy and roadmaps.

Own and improve Quora’s second-phase ranking models for feed and digest experiences. Build end-to-end machine learning systems covering data pipelines, candidate extraction, feature engineering, model training, and production integration. Identify new product applications for machine learning, develop efficient algorithms with other ML engineers, and contribute methodologies for large-scale recommendation systems.

Build and operate production machine learning systems for Safety AI, including low-latency APIs, data pipelines, model serving, evaluation, monitoring, and rollout infrastructure. Process large-scale camera and telematics data, optimize cloud and edge-to-cloud execution, track model performance and drift, and partner with applied scientists, firmware engineers, platform teams, and product managers to deliver reliable safety features.

Leads Hightouch’s machine learning organization, including product development, technical roadmapping, recruiting, reliability, and people management. The role focuses on personalization, product recommendations, probabilistic identity resolution, LLM-powered content generation, and scalable machine learning infrastructure for model training and inference.

Leads applied science strategy for AI-powered products, developing LLM and multimodal evaluation frameworks, offline experimentation pipelines, and production-quality AI systems. Defines success metrics, launch criteria, quality and safety standards, and monitoring practices. Partners with engineering and product leaders, drives model and product iteration, communicates technical findings, and mentors scientists and engineers.

Design and productionize causal machine learning models, including uplift modeling and treatment-effect estimation. Analyze A/B tests and quasi-experiments, develop experimentation strategies, evaluate modeling tradeoffs, and build scalable infrastructure. Collaborate with product and engineering teams, conduct code reviews, maintain engineering standards, communicate technical insights, and mentor others.

Develops and advances machine learning and deep learning models for personalized advertising recommendations. Responsibilities include understanding advertising systems and user behavior data, conducting foundational model research, validating approaches with large-scale compute, analyzing metrics, and collaborating with engineering and product teams to productionize research and deliver measurable business results.

Lead ML training performance for large-scale VLM, VLA, foundation-model, and reinforcement-learning systems. Design distributed parallelism, mixed-precision strategies, gradient checkpointing, GPU kernels, profiling workflows, and performance benchmarks. Own RL optimization strategy and roadmap, diagnose bottlenecks across compute and networking layers, partner with research and engineering teams on architecture, and mentor engineers.

Lead development and productionization of personalization and recommendation algorithms (ranking, retrieval, content/user understanding, post-ranking). Apply modern AI/LLM techniques, design and run offline and online experiments, drive technical vision and roadmap, collaborate with engineering/product/data teams, and build maintainable, production-grade ML systems at scale.

Leads the technical direction and end-to-end delivery of machine learning and LLM systems for merchant quality, integrity, relevance, and shopping discovery. Responsibilities include architecture, implementation, evaluation, experimentation, monitoring, safety, scalability, and production rollout. The role establishes ML engineering standards, partners cross-functionally, mentors engineers, influences technical roadmaps, and supports future hiring while improving user trust and shopping outcomes.

Develop and operationalize machine learning systems powering AI-driven insights from structured and unstructured financial data. Build NLP, generative AI, LLM, summarization, semantic search, classification, and prediction solutions. Responsibilities include model architecture, training, deployment, monitoring, optimization, scalable data pipelines, technical reviews, mentoring, research translation, and cross-functional collaboration with engineering, product, and data teams.