encord - Senior Machine Learning Engineer
Requirements
• An owner. You take ambiguous product needs, make the architecture calls and carry the work all the way into production. • A systems thinker. You design for scale and reuse, and you know when a simple, solid baseline beats a clever solution. • A great collaborator. You enjoy working across research, product and engineering, and you make it easier for everyone to ship. • A bridge between research and production. You speak both languages, help researchers get their work shipped, and feed production lessons back into how models are built. • 5+ years of software engineering experience, including meaningful time building and operating ML systems or model-backed products in production at scale • Strong software engineering and system design fundamentals • Hands-on experience deploying modern deep learning models in production, with a strong understanding of model serving, inference optimisation, GPU utilisation and performance trade-offs. • Experience designing and operating scalable, distributed production systems, ideally involving GPU workloads, high-throughput data processing or low-latency inference. • The ability to reason through an ML system end to end, from data and model choices to offline and online evaluation, deployment, monitoring and retraining • A track record of personally owning complex systems from design through to production • Bonus: computer vision or multimodal deployment experience (not required), model-serving frameworks, or experience with video, 3D or other complex data types
Responsibilities
• Design, build and operate model-serving and inference infrastructure for transformer-based vision and multimodal models • Own the serving architecture behind our ML features, justifying trade-offs in latency, throughput, reliability and GPU cost • Optimise models and pipelines for production through batching, KV caching, GPU utilisation and orchestration • Integrate models into Encord's product and annotation workflows together with Product Engineering • Build reusable abstractions that work across models, data types, customers and changing task templates • Set up monitoring and observability, and own performance and reliability once features are live • Take prototypes from your research-focused teammates and turn them into shipped features • Contribute to platform design decisions, take part in design reviews and help us hire • Work with Python, PyTorch, CUDA, Kubernetes and GCP/AWS
Benefits
• Competitive salary and meaningful equity in a high-growth startup • Strong in-person culture — most of the team works from our London office 4+ days/week • 25 days annual leave + UK public holidays • Annual learning & development budget • Travel for customer visits, events, and conferences across the UK and Europe • Daily company lunches • Monthly socials & bi-annual team offsites
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