UK remote
ML Engineer
About this role
CoreWeave is The Essential Cloud for AI™. Built for pioneers by pioneers, CoreWeave delivers a platform of technology, tools, and teams that enables innovators to build and scale AI with confidence. Trusted by leading AI labs, startups, and global enterprises, CoreWeave combines superior infrastructure performance with deep technical expertise to accelerate breakthroughs and turn compute into capability. Founded in 2017, CoreWeave became a publicly traded company (Nasdaq: CRWV) in March 2025.
Learn more at www.coreweave.com . We're proud to be a Living Wage accredited Employer. Machine Learning Engineer What You’ll Do: The Monolith Data Science team is building a layered reliability platform that shifts CoreWeave from reactive troubleshooting to proactive reliability engineering. The platform spans telemetry ingestion, feature engineering, anomaly detection, failure prediction, distributed straggler detection, and agentic root cause analysis.
As a Machine Learning Engineer, you will build the software, pipelines, and services that bring the team’s models into production and enable them to operate reliably at scale. Working closely with data scientists and the Fleet, Infrastructure, and AI Platform teams, you will help improve cluster reliability, increase effective utilization (MFU), reduce MTTR, and protect uptime and revenue. About the role: As a Machine Learning Engineer, you will focus on productionising and scaling machine learning solutions.
You will partner with data scientists who develop the models, building the engineering foundations needed to integrate those models into operational systems. You will develop data processing and inference pipelines, build services and APIs, and support deployment across production environments. You will improve performance, automate testing and deployment, and implement monitoring so that issues can be identified and resolved quickly.
This role combines hands-on software engineering with practical exposure to machine learning and large-scale infrastructure. You will help turn modeling outputs into dependable production capabilities, with a focus on maintainability, scalability, and integration into existing engineering workflows. Who You Are: Degree in Computer Science, Software Engineering, or a related technical field, or equivalent practical experience Strong proficiency in Python and experience writing clear, maintainable, and testable code Experience building, testing, and maintaining software in production environments Understanding of software engineering fundamentals, including code review, version control, automated testing, and debugging Experience developing APIs, backend services, or data processing pipelines Proficiency in SQL and experience working with structured datasets Ability to investigate software failures, identify performance bottlenecks, and implement practical improvements Understanding of reliability and scalability considerations when integrating software into production systems Ability to work closely with data scientists and infrastructure engineers to translate technical requirements into working software Clear communication skills, a collaborative approach, and a willingness to learn unfamiliar tools and systems Preferred: Experience deploying machine learning models or building model-serving and inference pipelines Familiarity with ML libraries such as scikit-learn, PyTorch, or TensorFlow Experience with Docker, Kubernetes, or cloud-native deployment environments Familiarity with CI/CD, production monitoring, logging, and observability Experience processing time-series data, system telemetry, or large-scale datasets using tools such as Spark, Ray, or Dask Exposure to GPU infrastructure, distributed computing, or reliability engineering Wondering if you’re a good fit? We believe in investing in our people, and value candidates who can bring their own diversified experiences to our teams - even if you aren't a 100% skill or experience match.