About GameChanger:

We believe in the life-changing impact youth sports have on and off the field because they 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:

We are seeking a skilled and driven MLOps Engineer to spearhead model deployments for Computer Vision and Machine Learning. The ideal candidate will have a strong background building machine learning infrastructure, deploying computer vision models, and managing the model lifecycle. Expertise in MLOps tools, machine learning frameworks like PyTorch or TensorFlow, and cloud platforms such as AWS is essential. Experience with mobile deployments to iOS and Android is also highly desirable. Once a part of our team, you’ll collaborate with a cross-functional team to bring solutions to life in a variety of sports, focusing mainly on basketball, baseball, and softball. This is a new team at GameChanger, and as such, there are exciting opportunities to work with senior leadership, lay the foundations for the future of CV in our product and in our engineering toolset.

What You’ll Do:

• Design and implement MLOps pipelines to automate model deployment to iOS, Android, and cloud infrastructure.

• Manage the end-to-end lifecycle of computer vision models, including testing, integration, release, continuous monitoring and scaling in cloud environments.

• Optimize cloud infrastructure for cost, performance, and efficiency.

• Collaborate with machine learning engineers and data scientists to ensure optimal model performance, scalability, and reliability.

• Develop reusable tools and frameworks to simplify future model deployments and reduce friction for other engineers.

• Stay up to date with the latest industry trends in MLOps and machine learning deployment technologies, bringing innovative solutions to enhance our capabilities.

• Help build a world-class ML practice at GC.

Who You Are:

• Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field.

• Track record of building robust maintainable machine learning infrastructure.

• Proven expertise in designing and managing large-scale, distributed machine learning systems.

• Ability to own projects from design to implementation, comfortable operating autonomously.

• Strong programming skills in Python and experience with popular computer vision and machine learning libraries such as PyTorch and Tensorflow.

• Strong communication skills, capable of sourcing technical requirements from multiple stakeholders.

• A forward-thinking engineer with a passion for building tools and systems that empower others.

Experience:

• Experience managing high throughput, scalable machine learning deployments, particularly in computer vision.

• Experience with containerization and orchestration technologies (e.g., Docker, ECS, Kubernetes).

• Familiarity with cloud services, Terraform and AWS experience preferred

• Proven ability to optimize and deploy models to iOS and Android 

• Understanding the pros and cons of running a model on the edge vs the backend, and how to help make those decisions a plus.

• Our backend APIs are built with TypeScript, Node.js, Redis, Kafka, and PostgreSQL and run in AWS. It’s not required that you know these, but we prefer that you are open to full-stack development.

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.

• Learning stipend - $500 annually towards continued development

• 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.

• Family building benefits offered through Progyny

• Life insurance - basic life, supplemental life, and dependent life.

• Disability leave - short-term disability and long-term disability.

• Retirement savings - Traditional and Roth 401K plans are offered through Vanguard, with an immediate company match.

• Generous parental leave.

• DICK’S Sporting Goods & their family of brands teammate discount.

The target salary range for this position is between $130,000 and $160,000. 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.

*DICK’S Sporting Goods has company-wide practices to monitor and protect us from compliance and monetary implications as it pertains to employer state tax liabilities. Due to said guidelines put in place, we are unable to hire in AK, DE, HI, IA, LA, MS, MT, OK, and SC.

We are an equal opportunity employer and value diversity in our company. We do not discriminate on the basis of race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status.

IMPORTANT NOTICE: All official recruitment communications from GameChanger will come from an email address ending in @gc.com or [email protected]. If you receive communication from any other domain, please be cautious, as it is likely fraudulent.