• Define and execute the company-wide quality engineering strategy. • Build and scale a high-performing global QA and Quality Engineering organization. • Establish quality standards, governance, metrics, and release readiness frameworks. • Drive a quality-first culture across Product, Engineering, and Delivery teams. • Lead the design and implementation of scalable automated testing frameworks. • Increase automation coverage across UI, API, integration, regression, and performance testing. • Partner with Engineering to embed quality throughout the software development lifecycle.

• QA team management: Provide leadership and mentorship to the QA team, ensuring they have the necessary resources, skills, and guidance to excel in their roles. • Allocate tasks and responsibilities among team members based on their strengths and expertise. • Hiring (tech interviews) & onboarding new employees • Probation period & performance review feedback, including performance issues handling • Quality Assurance Strategy: Develop and implement a comprehensive quality assurance strategy, including testing processes, methodologies, and tools to ensure high-quality product releases. • Process Improvement: Identify areas for process improvement within the QA team and across the development lifecycle, and implement best practices to enhance efficiency and effectiveness. • Test Planning and Execution: Oversee the creation and execution of detailed, comprehensive test plans and test cases for both manual and automated testing efforts. • Defect Management: Establish and maintain processes for logging, tracking, and prioritizing defects, working closely with development teams to ensure timely resolution. • Cross-Functional Collaboration: Collaborate with development, business, and other cross-functional teams to align quality assurance efforts with overall project goals. • Risk Assessment: Conduct risk assessments to identify potential areas of concern, and develop mitigation strategies to minimize features risks. • Reporting and Documentation: Generate and maintain comprehensive documentation of testing processes, results, and metrics for internal and external stakeholders. • Training and Development: Foster a culture of continuous learning and development within the QA team, providing coaching and training opportunities to enhance skills and knowledge.

Role Description 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. As the Lead QA Engineer, you’ll: - Provide technical leadership, delivering 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, being 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, working 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, being 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 - identifying and addressing potential obstacles to prevent issues before they ever reach production. - Coach and mentor, guiding 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. Qualifications - A proven leader with experience leading complex quality initiatives across platform and product teams. - Technical expertise in 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 who can influence and guide teams, helping them adopt best practices. - A problem-solver capable of autonomously identifying and solving critical quality challenges across complex, distributed ML systems. - MLOps or platform engineering experience with hands-on experience designing and building shared ML platform tooling. - A global mindset with strong remote collaboration skills. Requirements - ML sports industry experience is a bonus if you’ve used AI/ML in sports to generate data or create insights. Benefits - Champion work-life harmony with flexible vacation time, company-wide holidays, and remote work options. - Guarantee autonomy with an open, honest culture that trusts employees from day one. - Encourage career growth with resources and opportunities for professional development. - Provide an environment to help you succeed with well-designed office spaces and necessary tech. - Support your wellbeing with medical and retirement benefits, as well as resources for mental health support. 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. - Base Salary Range: £76,000 — £127,000 GBP Inclusion at Hudl Hudl is an equal opportunity employer. We create an environment where everyone feels like they belong, regardless of differences. We offer resources to ensure our employees feel safe bringing their authentic selves to work, including employee resource groups and communities. 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.

• Ending customer-reported bugs / by building an internal safety net that catches critical issues before release • Triage and reproduce incoming bug reports, and trace issues across logs, API calls, and session replays • Test features in active development and run a regression suite before every release • Establish QA sign-off as a standard part of the release process • Building the quality infrastructure from scratch / by defining the strategy, automation, and processes the team currently lacks • Define the testing strategy, standards, workflows, and documentation from the ground up • Design and implement an automation framework covering UI, API, and regression suites • Incrementally grow automated coverage of the highest-risk flows without disrupting delivery • Making quality a shared team capability / by embedding QA thinking across the development lifecycle • Track and report quality metrics so reliability is visible to the whole team • Translate customer bug reports into actionable test cases and durable coverage • Bring quality into requirements and design, not just release sign-off