Senior Applied Machine Learning Engineer, II

About GameChanger

We believe in the life changing impact youth sports have on and off the field. Sports encourage leadership, teamwork, responsibility, and confidence – important life lessons that have the power to propel our youth toward meaningful futures. We recognize that without coaches, parents, and volunteers, organized youth sports could not exist. By building the first and best place to experience the youth sports moments important to our community, we are helping families elevate the next generation through youth sports.

So if you love sports and their community building potential, or building cool products is your sport, GameChanger is the team for you. We are a remote first, dynamic tech company based in New York City, and we are solving some of the biggest challenges in youth sports today.

The Position

The Computer Vision Platform team at GameChanger builds and operates the real-time inference infrastructure that delivers CV-powered experiences to millions of users every weekend. You'll partner directly with researchers to shape their models and code for production, steer them toward existing platform capabilities, and collaborate with MLOps engineers to evolve infrastructure where gaps exist. You'll also influence the technical direction of the platform itself, identifying which capabilities to build next based on patterns you see across research efforts. You'll report directly to the Engineering Manager, CV Platform.

What You'll Do

• Work directly with CV researchers to understand their goals, review their code, and engineer it for reliability and performance at scale.
• Profile and optimize performance-sensitive code across both training and real-time inference.
• Identify patterns across research efforts and propose standardized, composable abstractions.
• Collaborate with MLOps and backend engineers to translate recurring research pain points into platform investments through design documents and architecture proposals.

• Guide researchers toward production-oriented engineering practices regarding testability, modularity, and performance profiling.

Who You Are

• 6+ years of software engineering experience, with 4+ years working on ML systems.
• Experience working on production ML systems. Our team uses Python and PyTorch, Docker for containerization, and deploys on AWS.
• Experience with data-intensive distributed systems, including optimizing I/O, GPU utilization, and parallel processing.
• Experience working directly with researchers or research teams to take their prototypes and deliver production systems that are performant, reliable, and maintainable.

• Experience building internal libraries, tools, or platforms that other engineers depend on.

Perks

• Work remotely throughout the US* or from our well-furnished, modern office in Manhattan, NY.
• Unlimited vacation policy.
• Paid volunteer opportunities.
• Technology stipend - $4,000 every 2 years after your start to make sure you have the latest and greatest technology.
• WFH stipend - $500 annually to make your WFH situation comfortable.
• Monthly physical, mental, wellness & learning stipend offered through Holisticly.
• Monthly lifestyle stipend offered through Fringe.
• Full health benefits - medical, dental, vision, prescription, FSA, HRA, HSA, and coverage for family/dependents.
• Retirement savings - Traditional and Roth 401K plans are offered through Vanguard, with an immediate company match.
• Life insurance - basic life, supplemental life, and dependent life.
• Disability leave - short-term disability and long-term disability.
• Company paid parental leave - up to 20 weeks for birthing parents and up to 12 weeks for non-birthing parents.
• Family building benefits offered through Progyny.
• DICK'S Sporting Goods and their family of brands teammate discount.

The target salary range for this position is between ( $180,000 and $200,000) This is part of a total compensation package that includes incentive, equity, and benefits for eligible roles. Individual pay may vary from the target range and is determined by several factors including experience, internal pay equity, and other relevant business considerations. We constantly review all teammate pay to ensure a great compensation package that is fair and equal across the board.