Principal ML Engineer

As a Principal Machine Learning Engineer, you’ll be hands-on in developing the future of AI-powered sports experiences. You’ll lead from the front—writing code, implementing core models, and building entire ML systems from scratch. Your work will bridge product vision and cutting-edge tech, combining real-time infrastructure with state-of-the-art ML to deliver tangible, high-impact features.

 

Responsibilities:

- Product-Driven Engineering Leadership: Translate ambitious product goals (real-time overlays, new analytics tools, 3D match replays) into technical designs and own the implementation

- End-to-End System Design: Build complete ML pipelines including data handling, training systems, model serving, and inference workflows

- Real-Time ML Systems: Design and implement low-latency infrastructure to enable live augmentations and instant analytics during games

- Product Collaboration: Work closely with product and business teams to shape new opportunities where our ML platform can unlock unique user experiences

- Autonomous Initiative Execution: Take full ownership of product-aligned ML projects—from vague problem statements to deployed systems supporting external users

- Cloud-Native ML Stack: Build reproducible, scalable ML workflows in public cloud environments (AWS/GCP), with a focus on GPU inference and sport-specific processing - Scalable Innovation: Push the limits of computer vision in sports while ensuring robustness and scalability across large datasets and competitions 

 

Who You Are:

- 5+ years building machine learning systems in production (beyond experimentation and prototyping)

- Skilled at balancing product context with deep technical execution—from early vision to live deployment

- Strong experience with computer vision—detection, segmentation, and possibly 3D modeling

- Able to turn loosely defined ideas into detailed implementation plans independently

- Passionate about working across the stack: from raw data to infrastructure and deployment

- Deep expertise in PyTorch, cloud-native workflows, and multi-GPU utilization

- Experience with real-time systems and solving low-latency technical challenges

- Strong interest in sports, especially football (soccer), or at least a willingness to dive into the domain Core 

Technical Requirements:

- Proven track record in building and deploying ML pipelines end-to-end in cloud environments

- Strong software engineering and system design skills, including distributed architecture

- Experience with streaming systems and real-time optimization

- History of launching production ML products at scale

- Deep knowledge of optimizing ML workloads: GPU usage, inference performance, distributed training

- Comfortable leveraging LLM-powered tools (e.g., Copilot, ChatGPT, Claude, Cursor) to accelerate development and exploration 

Nice to Have:

- Background in 3D graphics, scene reconstruction, or immersive/AR applications 

- Familiarity with video tech or broadcast media pipelines

- Domain experience in sports analytics, entertainment media, gaming, or betting

- Contributions to open-source ML projects or peer-reviewed publications

- Hands-on with frameworks such as YOLO, Detectron2, or the Hugging Face ecosystem