Senior Machine Learning Engineer

This is a hands on engineering role focused on building and running the systems that serve computer vision and multimodal models in production. A strong candidate brings years of software engineering plus real experience deploying deep learning models, tuning inference for speed and GPU cost, and owning distributed systems from design through live operation. The person also needs to work well with researchers and product teams, turning prototypes into dependable features.

How it fits the AI era

The work sits at the center of how AI changes annotation: models suggest labels in real time and learn from human corrections, while the engineer builds the serving layer that makes this fast and reliable. Much of the craft stays human, including architecture calls, trade offs between latency, throughput and cost, and judging when a simple baseline beats a clever one. The role is less about training models and more about making them dependable at scale.

How it builds AI experience

The job offers direct exposure to serving transformer based vision and multimodal models, inference optimisation, GPU orchestration and agentic quality control at production scale. Someone building toward this could practise deploying models with batching and caching, working with Kubernetes and cloud GPU workloads, and handling large video or sensor datasets. Experience with computer vision or multimodal deployment helps but is listed as a bonus rather than a requirement.

Worth showing

• Inference optimisation and GPU tuning
• Distributed systems at scale
• Owning systems end to end

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About Encord

A data platform that helps AI teams manage, label and clean the video, sensor and image data that trains robots, self-driving cars and other physical AI systems.