Engineering Manager - Machine Learning

Hudl; United States; Hybrid; USD 161,000 - 241,000; Full time

Job summary

Hudl is seeking an Engineering Manager for its Applied Machine Learning team to lead squads across American football and ice hockey. You\'ll drive ML initiatives, oversee model deployment and data-driven product insights for coaches during games and post-game analysis.

You\'ll mentor engineers and data scientists, balance model accuracy with scalability, and collaborate with product and cloud teams to align roadmaps and priorities.

Qualifications

• Experience leading teams of engineers and data scientists.
• Production AI/ML and computer vision experience.
• Experience deploying ML systems at scale.

Responsibilities

• Deliver for customers and manage teams across two squads.
• Drive technical and modeling excellence in cloud infrastructure.
• Lead your team to maintain model accuracy across real-world footage.
• Collaborate with product stakeholders and leaders to align priorities.
• Foster a high-performing, supportive environment for growth.

Skills

Team leadership

AI deployment

Computer vision

Cross-functional collaboration

Job description

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 the Top 100 Inspiring 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. Ready to join us?

We're hiring an Engineering Manager to join our Applied Machine Learning team to lead our squads across American football and ice hockey. You'll drive game-changing initiatives using computer vision and deep learning at scale to automatically generate game data, event detection and player tracking - delivering critical, real-time insights directly to coaches during games as well as powering post-game analysis products. Our systems process thousands of games in parallel during peak hours, using models grounded in recent research.

As the Engineering Manager, you'll:

• Deliver for customers and manage teams. Directly manage members across two established squads, helping them meet quarterly commitments, resolve unknowns and engage stakeholders to prioritise high-impact deliverables.
• Drive technical and modeling excellence. Set high standards across both engineering and data science, balancing model precision with execution speed and cost-effectiveness in cloud infrastructure.
• Solve complex real-world challenges. Lead your team to maintain model accuracy across diverse, real-world camera footage and environmental conditions.
• Collaborate cross-functionally. Work closely with product stakeholders, cross-functional partners and leaders to align roadmap priorities and resolve dependencies.
• Cultivate an empowered environment. Foster a high-performing, supportive environment that enables your squads to operate at their highest potential and grow professionally.

Location

Time spent collaborating with your teammates in-office will be a regular part of how you work. We're currently considering applicants within commuting distance of London. You'll be expected to work more days in-office than remote, but will have the freedom to coordinate your specific in-office schedule with your team. We're also open to remote candidates in the UK.

Must-Haves

• A proven leader. You have experience managing teams of individual contributors, including engineers and data scientists, and you love helping teammates grow.
• Production AI/ML and computer vision experience. You've built and deployed machine learning systems and computer vision models at scale in pro