Machine Learning Operations Engineer in Cambridge

Energy Jobline ZR; Cambridge; On-site; GBP 97,000 - 182,000; Full time

Job summary

A leading energy job board in the UK is seeking a Machine Learning Operations Engineer to develop platforms enabling advanced ML applications. Ideal candidates will have a strong background in Kubernetes and MLOps, with at least 3 years of relevant experience. This position offers a competitive salary range of $128,100 - $237,900 with potential for bonuses and extensive benefits.

Benefits

Annual bonus

Commuter cost subsidy

Medical benefits

Qualifications

• 3+ years of experience in MLOps, DevOps or related role.
• Strong hands-on experience deploying applications on Kubernetes.
• Experience developing MLOps platforms managing ML experiments.

Responsibilities

• Design, develop, and maintain platforms for ML applications.
• Enable self-service access to ML-compute clusters.
• Enhance observability across ML applications.

Skills

Kubernetes infrastructure management

ML-compute for on-prem and cloud clusters

Experiment tracking and management

Version control (Git)

CI/CD practices

Docker

Python environment management

Education

BS or MS in Computer Science or Engineering

Tools

Docker

Terraform

Job description

About the Company

Energy Jobline is the largest and fastest growing global Energy Job Board and Energy Hub. We have an audience reach of over 7 million energy professionals, 400,000+ monthly advertised global energy and engineering jobs, and work with the leading energy companies worldwide.

We focus on the Oil & Gas, Renewables, Engineering, Power, and Nuclear markets as well as emerging technologies in EV, Battery, and Fusion. We are committed to ensuring that we offer the most exciting career opportunities from around the world for our jobseekers.

Job Description

Our mission is to solve the most important and fundamental challenges in AI and Robotics to enable future of intelligent machines that will help us all live better lives.

Machine Learning Operations (ML-Ops) Engineers build infrastructure that supports the entire lifecycle of Machine Learning (ML) projects from development to scaling and to deployment. If you have a passion for building the foundation that enables robotics research and engineering, you will want to join us!

What You Will Do

• Design, develop, and maintain company-wide platforms and tooling that utilize Kubernetes infrastructure to enable machine learning and data processing applications
• Enable self-service access to ML-compute for our on-prem and cloud compute clusters, including support for job scheduling, workload scalability and workload fault tolerance
• Enhance observability across ML applications through integrations with tools and services such as FluentD, Prometheus, Grafana and DataDog
• Integrate ML applications with experiment tracking and management services like Weights and Biases
• Elevate code quality and champion best practices in our engineering processes
• Collaborate with Machine Learning Engineers, Data Engineers, DEVOPs engineers and researchers to build scalable solutions that improve engineering and research velocity.

What You Will Bring

• BS or MS in Computer Science, Engineering, or equivalent
• 3+ years of experience in an MLOPs, DevOps, ML Engineering or software engineering role
• Strong hands‑on experience deploying and managing applications running on Kubernetes
• Experience developing MLOPS platforms to manage the lifecycle of ML experiments; including one or more of data and artifact management, reproducibility, fault‑tolerance, experiment tracking and model serving
• Experience with Docker and Python environment management tools such as pip, poetry, uv or similar
• Proficient in software practices such as version control (Git), CI/CD (Github Actions, ArgoCD), Infrastructure as Code(Terraform).

Extra Skills We Value

• Experience with Kueue, or similar job scheduling mechanisms
• Experience with workflow orchestration tools such as Airflow, Metaflow, Argo Workflows or similar
• Hands‑on experience deploying and managing cloud infra on platforms like GCP and AWS
• Experience with hybrid‑cloud compute and data environments
• Experience with Ray, Pytorch Lightning or similar scalable AI/ML platforms
• Experience with application and system, logging with tools and services like FluentD, Prometheus, Grafana and DataDog or similar
• Experience with Bazel build tool or similar
• Experience with ML model serving frameworks such as Torchserve, ONNX runtime or similar
• Experience working with research teams in an academic or industrial environment.

Salary

The current reasonable and good faith estimate of the annual base salary range for this position is $128,100 - $237,900, which is based on a number of factors including, but not limited to, relevant skills and experience, educational background and certifications, performance and qualifications, market demand for the role, geographic location, and other organizational needs. An individual candidate may be considered for this position at a different job level, in which case the appropriate salary range will be provided to the candidate after their qualifications have been established.

Employees who are new to the RAI Institute typically receive an offer that is between the minimum and the midpoint of the posted salary range to allow for growth within the range over time.

Base pay is part of a competitive total compensation package that may include an annual bonus, a long‑term cash incentive, monthly cell phone cost reimbursement, commuter cost subsidy, medical benefits, and more.

Any final job offer will be determined based on our established compensation range for the role, the candidate’s experience and qualifications, internal parity, and market and business considerations. The advertised pay range is not a guarantee or promise of a specific wage.

EEO Statement

We provide equal employment opportunities to all employees and applicants for employment and prohibit discrimination and harassment of any type without regard to , , , , , , status, genetics, protected veteran status, , or expression, or any other characteristic protected by federal, state or local laws.

Apply

If you are interested in applying for this job please press the Apply Button and follow the application process. Energy Jobline wishes you the very best of luck in your next career move.