> Markdown version of [/jobs/ext/1307987-machine-learning-systems-engineer](https://www.wearedevelopers.com/jobs/ext/1307987-machine-learning-systems-engineer). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md).

---

# Machine Learning Systems Engineer

- **Company:** Unith
- **Location:** Barcelona, Spain (Remote available)
- **Salary:** €25,000.0 - €60,000.0
- **Contract:** Permanent contract
- **Skills:** A/B Testing, Application Programming Interfaces (APIs), Artificial Intelligence, Amazon Web Services, Amazon S3, Computer Vision, Continuous Integration, Software Debugging, Python (Programming Language), RabbitMQ, Redis, Tensorflow, Video Editing, WebRTC, Graphics Processing Unit (GPU), Real Time Systems, Pytorch, Flask (Web Framework), Caching, Backend, Fastapi, Event Driven Architecture, Kubernetes, Low Latency, ONNX (Open Neural Network Exchange) Format, Free and Open-Source Software, Machine Learning Operations, Video Streaming, Software Version Control, Docker
- **Published:** July 17, 2026
- **Apply:** https://www.adzuna.es/contact-us.html

## About the Role

+ 3-5 years of experience deploying ML/CV models to production (not just training)
 + Strong hands-on experience with PyTorch or TensorFlow
 + Practical optimization experience (quantization, pruning, model serving, GPU resource management)
 + Experience with video generation or real-time video processing and latency/quality trade-offs
 + Strong Python skills for backend services (FastAPI, Flask) and ML serving (TorchServe, ONNX Runtime)
 + Production infrastructure experience (Docker, AWS, CI/CD pipelines)
 + Strong debugging skills and ability to collaborate across research and backend teams
Nice-to-Have

 + Experience with audio-driven avatars, face reenactment, GANs, Diffusion Models, or NeRFs
 + gRPC, RabbitMQ, Go, or video streaming protocols (HLS, WebRTC)
 + Publications or open-source contributions in computer vision, + A short motivation (3-5 sentences) covering: - Your experience deploying CV/video models to production
 + One project where you reduced inference latency
 + Why digital humans excite you

## Description

Machine Learn Engineer, Video Generation, We're looking for an experienced Production ML Engineer to take ownership of our video synthesis pipeline. This is a hands-on, production-focused role where you'll bring AI research to life at scale.
You'll work at the intersection of computer vision, ML infrastructure, and real-time systems, ensuring our digital humans run reliably, efficiently, and with ultra-low latency - without sacrificing visual quality.
️ What You'll DoProduction Engineering (Core Focus)

 + Own production video synthesis services and deploy/optimize models for real-time performance
 + Reduce inference latency to meet a <2-second target for streaming conversations
 + Monitor and improve video quality metrics and debug production issues
 + Implement model versioning, A/B testing, and safe rollback procedures
Integration & Optimization

 + Act as the bridge between AI research and production systems
 + Integrate new models into the existing pipeline
 + Design video synthesis APIs (gRPC, REST) and work with event-driven architectures
 + Optimize GPU utilization, implement caching strategies, and collaborate on service orchestration
 + Handle TTS integration services (Voice Connectors)
Feature Development

 + Implement new visual features (expressiveness, movement, lip-sync improvements)
 + Support avatar customization capabilities
 + Production research enhancements into the real-time video pipeline
Tech Stack

 + Python, PyTorch, AWS, Docker, Kubernetes, GPU instances
 + gRPC services for streaming synthesis
 + S3, Redis, RabbitMQ, First 6 months - Ownership of core synthesis services with improved reliability and monitoring
 + Successful deployment of at least one research model into production
 + Measurable improvements in latency or video quality
First 12 months - Streaming video delivery with 30-50% latency reduction

 + Production rollout of visual improvements (expressiveness, movement)
 + Recognized as the go-to production ML expert bridging research and deployment
What We OfferCompensation & Flexibility


## Related Videos

- [Introduction to Azure Machine Learning](https://www.wearedevelopers.com/videos/368-introduction-to-azure-machine-learning)
- [Transforming Education: A Journey from interactive Markdown to Remote-Labs](https://www.wearedevelopers.com/videos/941-transforming-education-a-journey-from-interactive-markdown-to-remote-labs)
- [Docker Compose: Rediscovered](https://www.wearedevelopers.com/videos/1978-docker-compose-rediscovered)
- [Reducing LLM Calls with Vector Search Patterns - Raphael De Lio (Redis)](https://www.wearedevelopers.com/videos/1714-reducing-llm-calls-with-vector-search-patterns-raphael-de-lio-redis)
- [Shipping Faster with Less: Render on Cloud Hosting, AI Workloads, and the Future of DevOps](https://www.wearedevelopers.com/videos/1894-shipping-faster-with-less-render-on-cloud-hosting-ai-workloads-and-the-future-of-devops)
- [Hello JARVIS - Building Voice Interfaces for Your LLMS](https://www.wearedevelopers.com/videos/1641-hello-jarvis-building-voice-interfaces-for-your-llms)

## Related Articles

- [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer)
- [What Are Large Language Models?](https://www.wearedevelopers.com/magazine/304-what-are-large-language-models)
- [MLOps – What’s the deal behind it?](https://www.wearedevelopers.com/magazine/125-mlops-what-s-the-deal-behind-it)
- [MLops – Deploying, Maintaining And Evolving Machine Learning Models in Production](https://www.wearedevelopers.com/magazine/115-mlops-deploying-maintaining-and-evolving-machine-learning-models-in-production)
- [Navigating the AI Shift](https://www.wearedevelopers.com/magazine/629-navigating-the-ai-shift)
- [Coffee with Developers - Maria Apazoglou - Making AI understandable for all in production](https://www.wearedevelopers.com/magazine/475-coffee-with-developers-maria-apazoglou-making-ai-understandable-for-all-in-production)