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Machine Learning Operations Engineer

Vicon

Oxford

Hybrid

GBP 50,000 - 70,000

Full time

4 days ago
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Job summary

A leading motion capture technology company in Oxford is seeking an ML Ops Engineer to join their R&D team. The role involves managing Kubernetes clusters, developing ML operations infrastructures, and guiding project directions for innovative motion capture products. Competitive salary and hybrid working conditions offered.

Benefits

Competitive salary
10% Company Pension
25 days Annual Leave + Bank Holidays
Life Cover
Private Medical with Optical / Dental Insurance
Permanent Health Insurance
Cycle to work scheme
Free On‑site Parking

Qualifications

  • Relevant academic and/or industry experience in ML Ops.
  • Ability to manage Kubernetes clusters effectively.
  • Good programming skills in Python and familiarity with Linux.

Responsibilities

  • Support ML Development team in motion capture products.
  • Maintain modern ML Operations stack, including data pipelines.
  • Guide technical direction and lead projects.

Skills

Kubernetes management
Kubeflow knowledge
Python programming
AWS services experience
ML toolkits knowledge
MLOps design and implementation

Education

Research Master's degree or equivalent experience

Tools

AWS
PyTorch
MLflow
Job description

Department: Vicon Markerless

Location: Botley, Oxfordshire

Are you interested in working on products at the very cutting edge of technology? Want to join Vicon, an Academy Award‑winning company, and the world’s largest supplier of precision motion capture and tracking systems?

Vicon is the world leader in motion capture technology, developing high performance software and hardware products for the entertainment, engineering and life science industries. Vicon products are used in major feature films, games, and commercials, and are a crucial measurement tool for biomechanics, robotics, and cutting‑edge science.

Vicon is a subsidiary of Oxford Metrics Plc.

We are looking for an excellent ML Ops Engineer to join our research and development team in Oxford, England.

Key Responsibilities

This opportunity is to join the ML Operations teams which supports the ML Development team in building leading-edge motion capture products through provisioning and maintaining a modern ML Operations stack.

This stack covers data acquisition pipelines, data management and ML model training infrastructure (SW and on‑prem HW). We use both on‑prem, self‑managed systems and also leverage AWS infrastructure.

You will have opportunities to guide the technical direction of the ML Ops team, suggest new areas of development and the potential to lead your own project.

We offer a hybrid on‑site/home‑based working environment, with head office located in a major academic city. There is no expectation to be 'on call' outside core office hours.

Required Skills, Knowledge and Expertise

You will have relevant academic (research Masters level) and/or industry experience.

Essential Skills
  • Excellent knowledge and experience of managing an on‑premise Kubernetes cluster.
  • Excellent knowledge of Kubeflow and similar systems, e.g. MLflow
  • Good programming ability in Python with familiarity with Linux systems including scripting and system configuration.
  • Experience using AWS, e.g, Cognito, S3, EC2, Lambdas, etc.
  • Experience with ML toolkits, e.g. PyTorch, Lightning, etc., along with a solid understanding of how these fit into ML Ops pipelines and tools.
  • Be able to design and implement MLOps solutions covering many different technologies.
Desirable Skills
  • Background in DevOps with exposure to CI systems, e.g. Jenkins
  • Familiarity with infrastructure as code, e.g. Ansible
  • Experience, aptitude, and a desire to work with human motion capture, sport, animation tools and techniques.
  • Familiarity with C++.
Benefits
  • Competitive salary
  • 10% Company Pension
  • 25 days Annual Leave + Bank Holidays
  • Life Cover
  • Private Medical with Optical / Dental Insurance
  • Permanent Health Insurance
  • Cycle to work scheme.
  • Free On‑site Parking
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