Principal Software Engineer

As a Principal Software Engineer, you will shape and build the core backend systems that operationalize our computer vision models at scale. This is a hands-on, high-impact position—expect to be writing production code daily while also setting technical direction through what you build. Your work will span both large-scale batch systems and real-time streaming pipelines that power advanced media and sports data products.[Responsibilities]

Platform Architecture & Strategy: Design and build the full-stack infrastructure for data—from video ingestion through inference pipelines to product delivery layers
Scalable Systems Development: Engineer systems for two extremes—batch processing at massive scale and low-latency pipelines for live match augmentations
Product-Technical Leadership: Translate product goals into robust technical architecture; drive major projects from conception to deployment
Real-Time Infrastructure: Architect ultra-low-latency streaming components that enable live data overlays and in-broadcast enhancements
ML Platform Engineering: Develop infrastructure to support model deployment at scale, including GPU orchestration, optimized inference, and scalable serving
Initiative Ownership: Take end-to-end responsibility for complex, ambiguous technical challenges that unlock new product features
Distributed Systems Engineering: Build robust, cloud-native platforms designed for high availability, performance, and scale

Who You Are

5+ years building production-grade systems, with a focus on distributed architectures
Experience with both large-scale batch processing and real-time streaming pipelines
Python is your core language, but you're comfortable choosing the right tool for the job
Cloud-native thinking—comfortable with concepts like autoscaling, queues, SLAs
Deep familiarity with ML system challenges: GPU orchestration, inference performance, stateful services
Strong cross-functional communication—you can move fluidly between product conversations and technical deep-dives
You've scaled systems before and understand trade-offs between performance tuning and shipping fast

Core Technical Requirements

Expertise in distributed system design and implementation
Proven track record with both batch and real-time processing systems
Deep experience with cloud infrastructure and container orchestration (Kubernetes, Docker)
Production experience with high-throughput, low-latency system architecture
Familiarity with ML ops workflows: serving models, managing GPUs, building feature stores
Hands-on experience with infrastructure-as-code and modern DevOps practices
Comfortable using AI-assisted development tools (e.g., Copilot, ChatGPT, Cursor, Claude)

Nice to Have

Experience working with video streaming, processing, or broadcast systems
Background in real-time media tech or interactive sports/entertainment products
Understanding of computer vision pipelines and deep learning workflows
Contributions to open-source infrastructure or systems projects
Experience scaling platforms to support hundreds or thousands of concurrent users or compute processes

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