Machine Learning Jobs in Bellevue, WA

Leads the architecture and development of scalable, production-grade machine learning systems for personalization and recommendation experiences. Owns the full ML lifecycle, including data and feature pipelines, model training, deployment, serving, monitoring, retraining, and optimization. Establishes engineering standards for reliability, scalability, observability, and cost efficiency; partners with data science, engineering, product, and platform teams; develops reusable ML infrastructure; influences technical strategy; and mentors senior engineers.

The VP of Product will lead CoreWeave's Research Training Stack, focusing on enhancing AI infrastructure, ensuring quality for pre-trained models, and connecting researchers with advanced cloud capabilities.

Designs and operates large-scale AI infrastructure for GPU-based training and inference. Responsibilities include distributed training platforms, scheduling, storage and networking performance, observability, fault tolerance, security, automation, cost optimization, and developer tooling. Partners with ML research teams on capacity planning and maintains operational documentation. Requires expertise in GPU clusters, distributed systems, Linux, networking, cloud ML services, and software engineering, with proficiency in Python and Go or C++.

Build and maintain large-scale offline machine learning infrastructure, including training data pipelines, distributed training workflows, and multi-stage ML orchestration. Improve dataset validation, monitoring, testing, reproducibility, reliability, and performance. Collaborate with ML engineers and researchers to translate research ideas into scalable production systems and support model experimentation and iteration.

Designs, evaluates, and deploys applied machine learning systems across language, vision, recommendation, and structured-data domains. Responsibilities include developing scalable training and inference pipelines, conducting rigorous experiments, optimizing models, managing data quality, implementing safety and fairness evaluations, and translating research into production solutions. The role also involves collaborating with product and platform teams, documenting findings, mentoring engineers, and influencing AI strategy.

Design, build, and ship end-to-end personalization, ranking, and recommendation systems; prototype and scale new ML capabilities (including LLM fine-tuning and agentic workflows); partner with product and engineering to define strategy and deploy models to production at scale; lead architecture and code reviews, mentor engineers, and drive technical direction in a regulated fintech environment.

The Machine Learning Engineer will implement and evaluate ML algorithms, focusing on model development, A/B testing, and cross-functional collaboration to drive innovative solutions for financial challenges.

Full-cycle senior recruiter partnering with hiring managers to scope, source, evaluate, and close ML/AI and product talent. Use AI tools in recruiting workflows, manage high-stakes senior searches, collaborate cross-functionally, and provide candid communication to keep processes moving.

Contract senior recruiter responsible for sourcing and hiring ML/AI talent, managing full-cycle recruitment, partnering with hiring managers, and supporting diversity and employer-branding efforts.

Define and lead modeling architecture for personalization, matchmaking, recommendations, and social discovery. Build large-scale real-time inference systems, develop models for skill, behavior, trust & safety, and multi-objective optimization. Drive Responsible AI, experimentation, observability, and cross-functional standards while mentoring senior engineers and influencing ML strategy.

Develop and deploy generative AI and LLM solutions end-to-end for e-commerce: research models, build ETL/data pipelines, run distributed training and experiments, collaborate with product and engineering to deliver production systems that improve recommendations, search, personalization, and customer interactions.

Design, build, and operate large-scale offline data infrastructure and pipelines for ML training and experimentation. Improve reproducibility, observability, performance, and cost-efficiency across batch and streaming workloads while integrating orchestration systems and partnering with ML engineers.

Build and evaluate next-generation ranking and recommendation models using LLMs, RLHF, and preference learning. Develop large-scale user understanding systems, apply RL and optimization to bidding and delivery, design experiments with causal inference and A/B testing, and collaborate with engineering to deploy research into production while communicating results to technical and non-technical stakeholders.

Build and operate scalable data pipelines and feature datasets for ads ML. Own end-to-end offline flows from event ingestion to feature-ready datasets, develop batch and streaming systems, integrate feature store with online serving, improve observability and testing, and lead architectural decisions to ensure performance, correctness, and reliability at scale.

Lead architecture and technical strategy for production AI systems: prototype solutions, build reference implementations, define observability/evaluation/governance standards, bridge Applied AI and enterprise architecture, design integrations and cloud-native patterns, mentor engineers, and influence portfolio and partner decisions in a regulated healthcare environment.