Job Overview
Location
London, United Kingdom
Job Type
Full-time
Category
Data Science
Date Posted
June 22, 2026
Full Job Description
đź“‹ Description
- • Lead quality assurance initiatives across the Applied Machine Learning (AML) Platform squad and broader AML organization, ensuring ML products are shipped faster, safer, and with greater confidence from model development through scaled inference.
- • Design and build the quality infrastructure for the MLOps platform, including test frameworks, deployment safety tooling, and observability solutions, while owning the long-term technical strategy to scale with development velocity.
- • Collaborate across AML squads to align on quality standards for CI/CD pipelines, canary release patterns, and hardware-in-the-loop testing, ensuring platform solutions meet strategic objectives.
- • Act as a hands-on engineering contributor to the AML Platform Squad by writing code, designing systems, and implementing quality solutions that support reliable and efficient delivery.
- • Proactively identify and mitigate risks across the ML product lifecycle—including experimentation, deployment, inference, and observability—to prevent issues before they reach production.
- • Coach and mentor Software Engineers, Data Scientists, and QA practitioners on technical quality, automation, and best practices, fostering a culture of confidence through observability and innovation.
- • Drive adoption of advanced quality methodologies in ML and production engineering contexts, including automated rollback, A/B testing, and deployment safety patterns.
- • Serve as the first dedicated QA lead within the ML team, establishing foundational quality practices for a platform supporting over 230K sports teams across 40+ sports.
- • Work remotely or from the London office, collaborating effectively with a globally distributed team spanning the U.S. and Europe.
- • Ensure alignment between platform engineering goals and product team needs, balancing technical debt reduction with rapid iteration in a high-velocity ML environment.
- • Champion scalable, automated quality systems that reduce manual intervention and increase trust in ML model outputs across professional and amateur sports teams.
🎯 Requirements
- • MLOps or platform engineering experience with hands-on design and building of shared ML platform tooling such as experimentation infrastructure, model deployment pipelines, observability systems, or inference at scale.
- • Proven leadership in driving complex quality initiatives across platform and product teams, with the ability to define strategy for shared infrastructure serving multiple squads.
- • Technical expertise in CI/CD pipelines, deployment safety patterns (canary, A/B, automated rollback), and proactive adoption of quality methodologies in ML and production engineering.
- • Ability to influence and guide teams without direct authority, fostering adoption of best practices in quality, observability, and automation.
- • Strong problem-solving skills to autonomously resolve critical quality challenges in complex, distributed ML systems.
- • Excellent remote collaboration skills, with experience working across global teams in the U.S. and Europe.
🏖️ Benefits
- • Flexible vacation time beyond statutory requirements, including company-wide holidays and meeting-free timeout days.
- • Autonomy to own work and experiment with new ideas in an open, trust-based culture.
- • Resources and opportunities for professional development and lifelong learning.
- • Investment in office spaces and remote work technology to support high-performance work.
- • Support for wellbeing through Employee Assistance Program and employee resource groups, regardless of location.
- • Eligibility for long-term incentive (LTI) awards and performance-based bonuses.
Skills & Technologies
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About Agile Sports Technologies, Inc.
Agile Sports Technologies provides cloud-based video analysis and performance tools for sports teams and athletes. The platform combines video capture, editing, tagging, and distribution with statistical breakdowns and telestration to help coaches and players review plays, identify tendencies, and improve tactics. Users upload game and practice footage from mobile devices or cameras, then annotate key moments, create playlists, and share insights across teams. The service supports football, basketball, soccer, volleyball, and other sports at high school, collegiate, and professional levels, enabling remote collaboration and data-driven coaching decisions.
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