ML engineer in reliability platforms

Coreweave

Greater London

On-site

GBP 120,000 - 190,000

Full time

2 days ago

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Benefits offered by this job

Медицинское страхование семейногоLevel

Семейное стоматологическое страхование

Щедрый пенсионный взнос

Страхование жизни в 4x зарплаты

Страхование по критическим заболевания

Программа помощи сотрудникам

Возврат обучения

Культура работы, ориентированная на 혁신

Преимущества могут различаться по лока

Спасибо

Job summary

CoreWeave ищет специалиста по данным с опытом в ML и статистике для построения надежной платформы и улучшения использования GPU. Вы будете работать на стыке науки о данных и production‑систем, проектировать эксперименты, анализировать телеметрию и развивать обучающие модели в реальном времени.

Требуется степень MS/PhD и опыт работы в распределённых данных; предлагаются бонусы, опционы и пакет преимуществ.

Qualifications

MS or PhD in CS, Stats, or related quantitative field; 8+ years applying ML to large datasets.
Сильное владение Python, NumPy, pandas, SciPy, scikit-learn, PyTorch или TensorFlow.
Опыт проектирования и анализа контролируемых экспериментов (A/B тесты, причинностный вывод).
Опыт работы с распределёнными системами данных (Spark, Ray, Dask).

Responsibilities

Разработка и поддержка платформы надежности в продакшне.
Сотрудничество с командами Fleet, Infrastructure и AI Platform.
Проектирование экспериментов и анализ телеметрии больших данных.
Перевод аналитических выводов в производственные решения.

Skills

Python

NumPy

pandas

SciPy

scikit-learn

PyTorch

TensorFlow

A/B testing

causal inference

Spark

Ray

Dask

SQL

ML production

optimization

time-series

production workflows

Education

MS/PhD

Tools

Kubernetes

GCP/AWS

Job description

Описание:

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.

Задачи:

The Monolith Data Science team is building a layered reliability platform that shifts CoreWeave from reactive troubleshooting to proactive reliability engineering. As a forward deployed function, we partner closely with Fleet, Infrastructure, and AI Platform teams to embed data science directly into production environments—improving cluster reliability, increasing effective utilization (MFU), reducing MTTR, and protecting uptime and revenue. You will work at the intersection of data science and production systems, deploying advanced statistical models and machine learning methodologies directly within operational environments. You will collaborate closely with engineering and infrastructure teams to optimize GPU utilization, workload scheduling, and system efficiency in real time. You will design experiments, analyze large-scale system telemetry data, and build predictive and optimization solutions that are tightly integrated into production workflows. This role blends hands‑on deployment with analytical rigor, turning complex infrastructure data into measurable improvements in performance and cost. You will translate research and modeling insights into scalable, production‑ready systems.

Требования:

MS or PhD in Computer Science, Statistics, Applied Mathematics, Machine Learning, or related quantitative field 8+ years (or equivalent experience) applying statistical modeling or machine learning to large-scale datasets
Strong proficiency in Python and scientific computing libraries (NumPy, pandas, SciPy, scikit-learn, PyTorch or TensorFlow)
Demonstrated experience designing and analyzing controlled experiments (A/B testing, causal inference, hypothesis testing)
Experience working with distributed data systems (Spark, Ray, Dask, or similar)
Proficiency in SQL and working with large-scale structured datasets
Experience building, deploying, and maintaining predictive models in production environments
Strong understanding of optimization techniques (linear programming, convex optimization, stochastic optimization, or reinforcement learning)
Experience working with time-series data and performance telemetry
Ability to translate analytical insights into production‑ready systems and workflows
Strong collaboration skills and ability to work cross‑functionally in fast‑paced environments

Будет плюсом:

PhD with published research in systems optimization, distributed computing, ML systems, or performance modeling
Experience with GPU workloads, distributed training, or AI infrastructure
Familiarity with Kubernetes, containerized workloads, or cloud‑native systems
Experience deploying reinforcement learning or adaptive scheduling systems in production
Background in capacity planning, forecasting, or resource allocation modeling
Contributions to open‑source ML or systems projects

Условия:

The starting salary will be determined by job‑related knowledge, skills, experience, and the market location. We strive for both market alignment and internal equity when determining compensation. In addition to base salary, our total rewards package includes a discretionary bonus, equity awards, and a comprehensive benefits program (all based on eligibility).