Senior Python Engineer

Job Title: Principal Software Engineer

Overview:
As a Principal Software Engineer, you will shape and build the core backend systems that operationalize computer vision models at scale. This is a hands-on, high-impact position—expect to write 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 full-stack infrastructure for data, from video ingestion through inference pipelines to product delivery layers.

Scalable Systems Development

Engineer systems for both large-scale batch processing 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 for 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.
Experienced with both large-scale batch processing and real-time streaming pipelines.
Python is your core language, but you can select the right tool for the job.
Cloud-native thinker, familiar with autoscaling, queues, and SLAs.
Deep understanding of ML system challenges: GPU orchestration, inference performance, stateful services.
Strong cross-functional communicator, able to move between product discussions and technical deep-dives.
Experienced in scaling systems and understanding 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: model serving, GPU management, 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 with video streaming, processing, or broadcast systems.
Background in real-time media technology 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.