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Machine Learning Ops Engineer

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.

  • WFH stipend - $500 annually to make your WFH situation comfortable.

  • Snack stipend - $60 monthly to have snacks shipped to your home office.

  • Full health benefits - medical, dental, vision, prescription, FSA/HRA., and coverage for family/dependents.

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

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

  • Retirement savings - 401K plan offered through Vanguard, with a company match.

  • Company paid access to a wellness platform to support mental, financial and physical wellbeing.

  • Generous parental leave.

  • DICK’S Sporting Goods Teammate Discount.

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.

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.

IMPORTANT NOTICE: All official recruitment communications from GameChanger will come from an email address ending in @gc.com and/or no-reply@ashby.hq.com. If you receive communication from any other domain, please be cautious, as it is likely fraudulent.

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CEO of GameChanger
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Sameer Ahuja
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What You Should Know About Machine Learning Ops Engineer, GameChanger

At GameChanger, we're on a mission to redefine youth sports through technology, and we're looking for a passionate Machine Learning Ops Engineer to join our dynamic team in New York City! If you believe in the importance of community in sports and love developing innovative solutions, then this role is perfect for you. You'll get the chance to spearhead the deployment of cutting-edge Computer Vision and Machine Learning models, working closely with a talented cross-functional team to bring new ideas to life in basketball, baseball, and softball. Your expertise in MLOps tools and familiarity with platforms like AWS will be essential as you design and implement automation pipelines for model deployments across iOS, Android, and cloud infrastructures. You’ll oversee the entire lifecycle of computer vision models—from testing and integration to continuous monitoring—ensuring they perform optimally. At GameChanger, we value creativity and encourage you to stay ahead of industry trends while building a collaborative environment. You'll have the unique opportunity to shape our ML practices and lay the groundwork for future innovations. Plus, enjoy the perks of working remotely, generous vacation policies, and a commitment to your professional development and well-being. If you're ready to turn your passion for sports into impactful technology, GameChanger is where your journey starts!

Frequently Asked Questions (FAQs) for Machine Learning Ops Engineer Role at GameChanger
What are the primary responsibilities of a Machine Learning Ops Engineer at GameChanger?

As a Machine Learning Ops Engineer at GameChanger, you will be responsible for designing and implementing MLOps pipelines that automate the deployment of machine learning models to various platforms, including iOS and Android. You'll also manage the lifecycle of computer vision models, from testing and integration to release and scaling in the cloud. Collaborating closely with machine learning engineers and data scientists will be key to ensuring model performance and reliability.

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What qualifications are required for the Machine Learning Ops Engineer position at GameChanger?

To become a Machine Learning Ops Engineer at GameChanger, candidates should possess a Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field. Strong programming skills in Python, experience with MLOps tools, and expertise in machine learning frameworks like PyTorch or TensorFlow are essential. Additionally, familiarity with containerization technologies such as Docker and cloud platforms like AWS will enhance your application.

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How does GameChanger support the career growth of a Machine Learning Ops Engineer?

GameChanger values employee development and offers various opportunities for career growth, especially for the Machine Learning Ops Engineer role. You will work in a cross-functional team, gaining hands-on experience with state-of-the-art technologies while collaborating with senior leadership. Additionally, you will have access to resources that promote both technical skills and leadership capabilities, ultimately helping you build a successful and fulfilling career in tech.

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What does the team culture look like for a Machine Learning Ops Engineer at GameChanger?

At GameChanger, the culture is collaborative, innovative, and sports-focused. As a Machine Learning Ops Engineer, you will be welcomed into a supportive environment where ideas are shared, and every team member has a voice. We believe in creating a workplace where you can thrive, contribute to exciting projects, and enjoy the benefits of a remote-first setup or in our vibrant Manhattan office.

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What are the potential career paths after working as a Machine Learning Ops Engineer at GameChanger?

After gaining experience as a Machine Learning Ops Engineer at GameChanger, you can explore various career paths, including senior engineering roles, machine learning architect positions, or leadership roles within data science teams. With a solid foundation in model deployment and management, opportunities for specialization in AI and machine learning advancements are abundant, allowing you to shape your career in exciting directions.

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Common Interview Questions for Machine Learning Ops Engineer
Can you describe your experience with MLOps tools and their importance for a Machine Learning Ops Engineer?

When responding to this question, emphasize specific MLOps tools you’ve worked with, such as Kubeflow, MLflow, or Airflow. Discuss how these tools streamline the deployment and management of machine learning models, highlighting your direct experience and examples that demonstrate your effectiveness in using them to solve real-world problems.

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How do you approach optimizing cloud infrastructure for machine learning models?

Your answer should reflect a strategic mindset. Discuss your experience with cost management, performance improvements, and efficiency enhancements in cloud environments. Be prepared to provide concrete examples of how you have optimized the use of platforms like AWS or Azure in previous roles.

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What steps do you take to ensure the reliability and performance of computer vision models?

Outline a systematic approach, which includes regular monitoring after deployment, using metrics to evaluate model performance, conducting performance benchmarks, and having a rollback strategy for live models. Highlight any tools or frameworks you've utilized for model performance tracking.

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Have you ever faced challenges with model deployment? How did you address them?

Focus on a specific instance where you encountered significant hurdles during a deployment. Clearly explain the challenges, your thought process, and the steps you took to resolve the situation. Show your problem-solving skills and ability to remain calm under pressure.

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Can you share your experience with deploying models to mobile platforms?

Discuss any practical experiences you have in deploying machine learning models to iOS and Android, including the strategies you used and the tools that facilitated these deployments. Be sure to mention any challenges faced and how you overcame them to achieve successful results.

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How do you ensure compliance and security when deploying ML models?

Address the importance of compliance with data protection regulations and security standards. Illustrate your experience with best practices for securing data, algorithms, and infrastructures during deployments, along with any relevant compliance frameworks you are familiar with.

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Describe how you would communicate technical requirements to non-technical stakeholders.

Communicating technical terms with clarity is vital. Explain your approaches, such as simplifying complex concepts, using visual aids, and ensuring you understand the audience’s needs. Providing an example of successful communication with a non-technical team can strengthen your response.

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What are your thoughts on edge computing versus cloud computing for model deployment?

In your response, convey a balanced understanding of the pros and cons of both methods. Discuss scenarios in which each option is preferable based on factors such as latency, data sensitivity, and available infrastructure, supporting your insights with relevant examples.

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How do you stay updated with the latest trends in MLOps and machine learning technologies?

Indicate your commitment to continuous learning. Mention preferred resources like online courses, conferences, industry blogs, or active participation in relevant communities. This demonstrates your passion and proactive approach to staying informed about advancements in your field.

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What do you consider as key challenges in building a robust MLOps pipeline?

Analyze common challenges like version control for models, reproducibility, and training data management. Provide your opinion on how to effectively manage these issues and share any personal experiences where you successfully tackled similar challenges in past projects.

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GameChanger provides teams across the country with live video streaming, team management and scorekeeping capabilities. We connect families, friends and generations to follow their athletes’ journey in a simple and powerful way.

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Full-time, remote
DATE POSTED
January 10, 2025

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