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Principal Software Engineer
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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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