Lead Quality Assurance Engineer - Machine Learning

Lead Quality Assurance Engineer - Machine Learning

Full-Time 80000 - 100000 £ / year (est.) Home office (partial)
Hudl

At a Glance

  • Tasks: Lead quality assurance for machine learning products and enhance team collaboration.
  • Company: Join Hudl, a top-rated workplace dedicated to sports innovation.
  • Benefits: Flexible work options, competitive salary, and professional development opportunities.
  • Other info: Dynamic environment with a focus on work-life balance and employee wellbeing.
  • Why this job: Make a real impact in sports tech while working with cutting-edge ML solutions.
  • Qualifications: Experience in MLOps, leadership skills, and a passion for quality assurance.

The predicted salary is between 80000 - 100000 £ per year.

At Hudl, we build great teams. We hire the best of the best to ensure you’re working with people you can constantly learn from. You’re trusted to get your work done your way while testing the limits of what’s possible and what’s next. We work hard to provide a culture where everyone feels supported, and our employees feel it—their votes helped us become one of Newsweek's Top 100 Global Most Loved Workplaces. We think of ourselves as the team behind the team, supporting the lifelong impact sports can have: the lessons in teamwork and dedication; the influence of inspiring coaches; and the opportunities to reach new heights. That’s why we help teams from all over the world see their game differently. Our products make it easier for coaches and athletes at any level to capture video, analyze data, share highlights and more.

Your Role

Our Applied Machine Learning (AML) team’s vision is to extract valuable insights from video and deliver them to coaches, athletes, and fans at the perfect moment; serving over 230K sports teams across 40+ sports, including 11K+ professional teams. We’re looking for a Lead Quality Assurance Engineer to join our AML Platform squad - the team that designs, builds, and operates our shared MLOps platform, reducing delivery friction, and acting as a force-multiplier for every squad we support.

Responsibilities

  • Provide technical leadership. You’ll deliver quality leadership across the AML Platform Squad and broader AML organization, helping squads ship ML products faster, safer, and with greater confidence - from model development through to scaled inference.
  • Own the architecture and strategy. You will be hands‑on in designing and building our quality infrastructure across the MLOps platform - architecting test frameworks, deployment safety tooling, and observability solutions - while owning the long‑term technical strategy to ensure it scales with development speed.
  • Collaborate across departments. You’ll work across AML squads to drive alignment on quality initiatives from CI/CD pipeline standards to canary release patterns and hardware‑in‑the‑loop testing, ensuring scalable platform solutions meet long‑term strategic objectives.
  • Build and deliver. You’ll be a hands‑on contributor to the AML Platform Squad’s engineering work - writing code, designing systems, and implementing quality solutions - while ensuring long‑term objectives stay aligned with reliable, efficient delivery.
  • Drive proactive quality. By anticipating risks across the ML product lifecycle - experimentation, deployment, inference, and observability - you’ll identify and address potential obstacles to prevent issues before they ever reach production.
  • Coach and mentor. You’ll guide Software Engineers, Data Scientists, and QA practitioners on technical quality and innovation, fostering an environment where your teammates and the squads we support can grow and succeed.

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 U.K.

Must-Haves

  • MLOps or platform engineering experience. You have hands‑on, practical experience designing and building shared ML platform tooling — experimentation infrastructure, model deployment pipelines, observability systems, or inference at scale. As you'd be the first QA in our ML team, this knowledge is essential.
  • A proven leader. You have experience leading complex quality initiatives across platform and product teams, with the ability to define strategy for shared infrastructure that serves multiple squads simultaneously.
  • Technical expertise. You have a deep understanding of CI/CD pipelines, deployment safety patterns (canary, A/B, automated rollback), and a proactive approach to adopting new quality methodologies in ML and production engineering contexts.
  • An expert coach. You know how to influence and guide teams even when they don’t report to you, helping them adopt best practices and fostering a mindset of quality, observability, and confidence-through-automation.
  • A problem‑solver. You can autonomously identify and solve critical quality challenges across complex, distributed ML systems - navigating both platform and product concerns to keep AML squads on track.
  • Global mindset. Our ML team is spread across the U.S. and Europe, so strong remote collaboration skills are a must.

Nice‑to‑Haves

  • ML sports industry experience. It’s a bonus if you’ve used AI/ML in sports to generate data or create insights.

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

The base salary range for this role is displayed below—starting salaries will typically fall near the middle of this range. We make compensation decisions based on an individual's experience, skills and education in line with our internal pay equity practices. This role will also be eligible for a long‑term incentive (LTI) award. Any bonuses awarded are based on individual and company performance paid at Hudl's discretion.

Inclusion at Hudl

Hudl is an equal opportunity employer. Through our actions, behaviors and attitude, we’ll create an environment where everyone, no matter their differences, feels like they belong. We offer resources to ensure our employees feel safe bringing their authentic selves to work, including employee resource groups and communities. But we recognize there’s ongoing work to be done, which is why we track our efforts and commitments in annual inclusion reports. We also know imposter syndrome is real and the confidence gap can get in the way of meeting spectacular candidates. Please don’t hesitate to apply—we’d love to hear from you.

Lead Quality Assurance Engineer - Machine Learning employer: Hudl

At Hudl, we pride ourselves on fostering a supportive and innovative work culture that empowers our employees to excel. As a Lead Quality Assurance Engineer in London, you'll enjoy flexible working arrangements, ample opportunities for professional growth, and the chance to collaborate with top talent in the sports technology industry. Our commitment to employee wellbeing and autonomy ensures that you can thrive both personally and professionally while contributing to meaningful projects that impact teams worldwide.

Hudl

Contact Details:

Hudl Recruitment Team

StudySmarter Expert Advice🤫

We think this is how you could land Lead Quality Assurance Engineer - Machine Learning

Join Local Tech Meetups

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Contribute to Open Source Projects

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We think you need these skills to ace Lead Quality Assurance Engineer - Machine Learning

MLOps
Platform Engineering
CI/CD Pipelines
Deployment Safety Patterns
Quality Methodologies
Coaching and Mentoring
Problem-Solving

Some tips for your application 🫡

Show off your coding skills:When applying for a software engineering role, it's super important to showcase your coding skills. Make sure your CV includes your tech stack, any relevant programming languages you’re comfortable with, and examples of projects you've worked on. If you have a GitHub profile, link it up! We love to see code in action.

Tailor your portfolio:For a full-time role, we’d expect to see some solid examples of your work in your portfolio. Make sure to include at least two or three projects that highlight your problem-solving skills and your ability to work with different technologies. Focus on the projects that are most relevant to the position at Hudl.

Craft a killer cover letter:Your cover letter is your chance to stand out—make it personal! Explain why you want to work at Hudl and how your skills align with the role. Show us your passion for software development. We dig enthusiastic candidates who understand the value of collaboration and continuous learning!

Be clear and concise:When it comes to writing your CV and cover letter, clarity is key. Avoid jargon that could confuse us and stick to simple, direct language. Highlight your achievements with quantifiable results where possible, and keep everything easy to read. A well-organised application goes a long way!

How to prepare for a job interview at Hudl

Brush Up on Your Coding Skills

For a full-time software engineering role, it's crucial that we stay sharp with our coding abilities. Expect technical questions that might involve solving problems on the spot or discussing algorithms. Practise on platforms like LeetCode or HackerRank to get comfortable with the types of questions that often come up.

Know Your Tools and Frameworks

Make sure we’re well-acquainted with the tools and technologies listed in the job description. Familiarise ourselves with any specific frameworks or programming languages mentioned. If Hudl uses React or Node.js, for instance, be ready to discuss how we’ve used them in previous projects or coursework.

Showcase Your Projects

Bring along a portfolio that highlights our best work. This could be code samples, GitHub repositories, or any side projects we’ve built. Make sure we can talk through our thought process for each project, especially the challenges we faced and how we solved them—this shows our problem-solving skills in action.

Prepare for Behavioural Questions

While technical skills are key, full-time positions also require cultural fit. Be ready to discuss our previous experiences and how we handle teamwork, conflict, and deadlines. Brush up on the STAR method—Situation, Task, Action, Result—to clearly articulate our past experiences when discussing how we've contributed to a team.