Machine Learning Systems Engineer
Role details
Job location
Tech stack
Job 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)
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Own production video synthesis services and deploy/optimize models for real-time performance
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Reduce inference latency to meet a <2-second target for streaming conversations
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Monitor and improve video quality metrics and debug production issues
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Implement model versioning, A/B testing, and safe rollback procedures Integration & Optimization
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Act as the bridge between AI research and production systems
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Integrate new models into the existing pipeline
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Design video synthesis APIs (gRPC, REST) and work with event-driven architectures
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Optimize GPU utilization, implement caching strategies, and collaborate on service orchestration
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Handle TTS integration services (Voice Connectors) Feature Development
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Implement new visual features (expressiveness, movement, lip-sync improvements)
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Support avatar customization capabilities
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Production research enhancements into the real-time video pipeline Tech Stack
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Python, PyTorch, AWS, Docker, Kubernetes, GPU instances
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gRPC services for streaming synthesis
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S3, Redis, RabbitMQ, First 6 months - Ownership of core synthesis services with improved reliability and monitoring
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Successful deployment of at least one research model into production
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Measurable improvements in latency or video quality First 12 months - Streaming video delivery with 30-50% latency reduction
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Production rollout of visual improvements (expressiveness, movement)
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Recognized as the go-to production ML expert bridging research and deployment What We OfferCompensation & Flexibility
Requirements
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3-5 years of experience deploying ML/CV models to production (not just training)
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Strong hands-on experience with PyTorch or TensorFlow
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Practical optimization experience (quantization, pruning, model serving, GPU resource management)
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Experience with video generation or real-time video processing and latency/quality trade-offs
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Strong Python skills for backend services (FastAPI, Flask) and ML serving (TorchServe, ONNX Runtime)
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Production infrastructure experience (Docker, AWS, CI/CD pipelines)
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Strong debugging skills and ability to collaborate across research and backend teams Nice-to-Have
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Experience with audio-driven avatars, face reenactment, GANs, Diffusion Models, or NeRFs
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gRPC, RabbitMQ, Go, or video streaming protocols (HLS, WebRTC)
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Publications or open-source contributions in computer vision, + A short motivation (3-5 sentences) covering: - Your experience deploying CV/video models to production
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One project where you reduced inference latency
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Why digital humans excite you
Benefits & conditions
- End-to-end ownership of critical production systems
- Unique role bridging cutting-edge CV research and real-world deployment
- Challenging problems in real-time ML, latency optimization, and scalability
- High-impact work in a small, senior team (12 people)
- Opportunity to shape ML infrastructure as the company scales Additional Perks ️ Office in the center of Barcelona Work from anywhere ️ Lunch compensation when in the office Private health insurance with Alan Travel allowance (for team members living 10km+ from the office) Flexible benefits (tax-free under Spanish legislation) ️ ClassPass discount