Computer Vision Lead

Role Overview:

We are looking for a Computer Vision Lead to head the vision and multimodal AI charter behind our next-generation sports AI products. This is a hands-on leadership role: you will own the technical direction of our real-time sports understanding stack, set the architecture and research agenda, and build and mentor a high-performing team of Computer Vision and AI Engineers. You will be accountable for taking models from research to reliable, low-latency production systems experienced by millions of fans.

Key Responsibility Areas:

1. Technical Leadership & Team Building

- Own the technical vision, architecture, and roadmap for Computer Vision and multimodal AI across the product portfolio.

- Lead, mentor, and grow a team of Computer Vision and AI Engineers; drive hiring, onboarding, and capability development.

- Set engineering standards for experimentation, code quality, documentation, reproducibility, and model governance.

- Translate product goals into technical milestones, effort estimates, and delivery plans; own execution end to end.

- Make build-vs-buy and architecture trade-off decisions across model, infrastructure, and vendor choices.

2. AI Product Development & Language Intelligence

- Define the strategy for AI-powered language products that enhance sports content creation and fan engagement.

- Lead development of automated live commentary systems using Large Language Models (LLMs), multimodal AI, and speech technologies.

- Architect intelligent pipelines that fuse vision, audio, and contextual match data into real-time insights and narratives.

- Evaluate and adopt emerging AI architectures to keep product capabilities ahead of the market.

3. Computer Vision & Model Development

- Direct the design, training, and optimization of computer vision models for live sports analytics.

- Guide algorithm development for image and video understanding, including player, ball, and object tracking.

- Set architecture direction across CNNs, Vision Transformers (ViTs), YOLO, Faster R-CNN, Mask R-CNN, and vision-language models.

- Oversee object detection, image classification, segmentation, pose estimation, OCR, facial recognition, and event detection workstreams.

- Establish data strategy: annotation pipelines, augmentation, dataset quality, and data engineering best practices.

- Define evaluation frameworks and benchmarks for accuracy, latency, and robustness; drive continuous improvement against them.

4. Deployment, Performance Optimization & Production Engineering

- Own the deployment architecture for models across cloud, edge devices, and GPU-enabled environments.

- Architect scalable inference pipelines with high throughput and low latency for real-time applications.

- Drive deployment practices using Docker, ONNX, TensorRT, FastAPI, Kubernetes, and edge GPU platforms.

- Ensure robust integration into production systems through APIs, microservices, and modern software engineering practices.

- Establish monitoring, observability, and MLOps practices for production reliability and cost efficiency.

5. Innovation, Collaboration & Research

- Partner with Product, Design, Data Science, and Software Engineering leadership to shape the product roadmap.

- Lead applied research in Computer Vision, Multimodal AI, Generative AI, and Sports Analytics; identify what is worth productionizing.

- Represent the team in cross-functional and leadership forums; communicate technical trade-offs to non-technical stakeholders.

- Champion engineering excellence through architecture reviews, design reviews, and code reviews.