Principal Software Engineer

ReSpo.Vision is an AI and Computer Vision company transforming how sports are analyzed, visualized, and monetized. Our proprietary single-camera system extracts elite-level tracking data and performance analytics from standard broadcast or tactical video, without wearables or in-venue installations. Already used by global clubs, federations like FIFA, Concacaf, or CONMEBOL and for competitions like Euro or Copa America, we are actively expanding into media, fan engagement, and betting applications.

Our pipeline combines advanced computer vision and deep learning models to track all players and the ball in 3D using a single-camera feed. The resulting positional data powers our growing product suite: from match analytics platform to visual content, including 3D match reconstruction (see an early demo: YouTube) and real-time broadcast augmentation with dynamic stats and virtual overlays. The system is built for scalability, leveraging cloud-native infrastructure, GPU inference pipelines, and sports-specific post-processing modules that turn raw detections into meaningful insights.

We’re now entering an exciting phase, pushing our tracking system toward real-time applications, lowering latency to enable live insights and instant visual augmentations. In parallel, we’re building new layers on top of our core data, including realistic 3D match reconstructions and virtual overlays for enhanced broadcast experiences.

Your Role

As a Principal Software Engineer at ReSpo.Vision, you'll architect and build the core infrastructure that transforms our computer vision models into scalable, production-ready systems. This is a hands-on role where you'll write code daily, implement critical system components yourself, and contribute directly to our technical strategy through what you build. You'll personally develop both large-scale batch processing pipelines and real-time streaming systems that power our media and sports analytics products.

You will be responsible for:

1. Platform Architecture & Strategy: Design and implement the end-to-end data platform architecture, from video ingestion through processing pipelines to client delivery systems
2. Scalable Systems Development: Build infrastructure for both use-cases - batch processing of thousands of historical matches and sub-second latency for live match augmentation
3. Product-Technical Leadership: Translate product vision into technical architecture, independently driving initiatives from concept to production deployment
4. Real-Time Infrastructure: Architect low-latency streaming systems that enable instant analytics and broadcast augmentations during live matches
5. ML Platform Engineering: Design and optimize the infrastructure layer for ML model deployment, including GPU orchestration, inference optimization, and scalable serving systems
6. Technical Initiative Ownership: Lead complex, ambiguous projects autonomously - identifying technical opportunities that unlock new product capabilities
7. Distributed Systems Excellence: Build fault-tolerant, cloud-native systems that are scalable and performant

Who You Are

1. You've spent 5+ years building production systems, with at least some years designing distributed architectures
2. You know how to build for both extremes: batch systems processing terabytes AND real-time systems with millisecond budgets
3. Python is your go-to, but you pick the right tool for the job
4. Cloud native - you think in terms of auto-scaling groups, message queues, and SLAs
5. You understand the specific challenges of ML systems: GPU scheduling, statefulness, inference optimization
6. You can talk to product managers about features and to engineers about implementation - and enjoy both conversations
7. You've been through the scaling pain before and know when to optimize vs when to ship

Core Technical Requirements

1. Extensive knowledge of distributed systems design and implementation
2. Proven experience with both batch processing and streaming architectures
3. Expertise in cloud infrastructure and containerization (Kubernetes, Docker)
4. Experience building low-latency, high-throughput systems in production
5. At least some background in ML infrastructure: model serving, feature stores, GPU optimization
6. Proficiency in infrastructure as code and modern DevOps practices
7. Effective use of LLM-based tools (Copilot, Cursor, Claude, ChatGPT etc.) to accelerate development and research workflows

Nice to have

1. Experience with video processing or broadcast streaming infrastructure
2. Background in real-time data systems or media/entertainment and/or sports technology/products
3. Knowledge of computer vision and DL/ML pipelines
4. Contributions to open-source infrastructure projects
5. Experience scaling systems to hundreds/thousands of concurrent users/processing instances

What we offer

1. A chance to work with a top-tier engineering team, including Kaggle Grandmasters
2. Hybrid work model
3. Flexibility in employment type (B2B/contract of employment)
4. Market-competitive salary
5. Private healthcare and Multisport card
6. Open training budget – we’ll support your development in relevant areas
7. Ownership and autonomy – no micromanagement, real impact
8. A unique opportunity to shape a globally recognized, high-impact product used by top sports organizations like Chelsea, Paris Saint-Germain, or FIFA