Senior MLOps Engineer

Your Role

We’re hiring a Senior MLOps Engineer to join our Global Football Metrics group, where you’ll build and scale the machine learning infrastructure that powers next-generation sports analytics. You’ll own the MLOps pipelines that transform raw data and ML models into production-ready insights used by professional teams worldwide.

As a Senior MLOps Engineer, you’ll:

• Build scalable ML infrastructure. You’ll design, develop and maintain the MLOps platforms and pipelines that enable our data science teams to train, deploy and monitor machine learning models at scale while working across the full ML lifecycle.
• Work with cross-functional teams. You’ll collaborate with Data Scientists, ML Engineers, Software Engineers, Product and Platform teams to deliver robust, automated ML systems that bridge the gap between research and production.
• Drive automation and efficiency. You’ll implement CI/CD pipelines for ML models, automate retraining workflows and build monitoring systems to ensure reliability as you deploy changes hundreds of times daily.
• Solve complex technical challenges. You’ll tackle ambiguous infrastructure problems, evaluate new MLOps tools and architect solutions that enable our data science teams to work faster and more effectively.
• Mentor and lead. You’ll share your MLOps expertise to establish best practices and guide other engineers on topics like model versioning, experiment tracking and feature stores.

We'd like to hire someone for this role who lives near our office in London, but we're also open to remote candidates in the UK. 

Must-Haves

• Experienced in production ML systems. You’ve played a key role in building and operating large-scale machine learning infrastructure and understand the challenges of moving models from notebooks to production.
• Technical expertise. You write clean, maintainable code and understand software engineering best practices, plus you have hands-on experience with containerisation, orchestration tools, CI/CD pipelines, and infrastructure-as-code.
• Collaborative. You understand that building ML systems is a team sport and work effectively with cross-functional partners to translate requirements into scalable solutions.
• User-focused. You’re motivated by building systems that help real people solve real problems, caring about the experience of both internal data scientists and external customers.

Nice-to-Haves

• MLOps tooling experience. Expeirence with MLflow, Kubeflow, Airflow, Feast, DVC, Weights & Biases or similar ML platforms would be great.
• Tech stack knowledge. Experience with Python, Kafka, PostgreSQL, Redshift, S3, SageMaker or AWS infrastructure is a plus.
• Sports analytics passion. You have an interest in sports data, video analytics or performance metrics—but if not, we’ll teach you the domain. 

Our Role 

• Champion work-life harmony. We’ll give you the flexibility you need in your work life (e.g., flexible vacation time above any required statutory leave, company-wide holidays and timeout (meeting-free) days, remote work options and more) so you can enjoy your personal life too.
• Guarantee autonomy. We have an open, honest culture and we trust our people from day one. Your team will support you, but you’ll own your work and have the agency to try new ideas.
• Encourage career growth. We’re lifelong learners who encourage professional development. We’ll give you tons of resources and opportunities to keep growing.
• Provide an environment to help you succeed. We've invested in our offices, designing incredible spaces with our employees in mind. But whether you’re at the office or working remotely, we’ll provide you the tech you need to do your best work.
• Support your wellbeing. Depending on location, we offer medical and retirement benefits for employees—but no matter where you’re located, we have resources like our Employee Assistance Program and employee resource groups to support your mental health.

Compensation