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Today at Wayve, our model development cycle is composed of multiple complex training phases, each building on the last. As a Staff Machine Learning Engineer (Ops/Release), you are expected to have a deep understanding of each of the training phases, understanding how our models are created from start to finish. You will ultimately be responsible for setting and enforcing the standard of each of the release gates along this journey, ensuring that each training phase is sufficiently validated before the next phase begins.
You'll drive technical excellence across our ML delivery pipelines. You'll review release content to ensure it meets our standards, identify bottlenecks in the process, and partner with platform teams to make sure tooling meets our delivery needs. You'll work with CI/CD teams to adapt workflows and streamline model delivery, and with evaluation teams to keep our methods reliable — spotting gaps and driving new methodology for evaluating our models.
This is a high-trust, high-visibility role: our release process directly protects our model baseline, and a mistake here has real consequences for how the product performs on-road and how it's perceived externally.
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In order to set you up for success as a Staff Machine Learning - Ops at Wayve, we’re looking for the following skills and experience.
Full system thinker with experience of introducing operational processes to build engineering excellence.
Strong ML Ops, model registry and ML lifecycle experience
A deep technical depth in ML training
A strong understanding of ML code infrastructure and best practices – experience with pytorch, tensor RT, quantisation and model deployment
Strong CI/CD and Github Actions experience
Strong communications skills with a collaborative mindset
Experience with Pytorch, TensorRT, quantisation and model deployment
Experience with Grafana monitoring and production observability
This is a full-time role based in our office in London. At Wayve we want the best of all worlds so we operate a hybrid working policy that combines time together in our offices and workshops to fuel innovation, culture, relationships and learning, and time spent working from home.
British autonomous-driving software company licensing vehicle-agnostic AI Driver technology to automakers and fleet owners.
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