Machine Learning Team Lead

Speechmatics Sourced

Cambridge, United Kingdom Full-time Not specified

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About the role

We are hiring a Machine Learning Team Lead to lead a team of driven ML engineers working on translating cutting-edge research into ambitious, customer-centric speech solutions.  As innovators in speech technology, our mission is to Understand Every Voice — a vision that has propelled us to be world leaders of Speech AI. Fuelled by innovation, inclusivity, and a passion for making a global impact through world-leading Speech AI, we're looking for an experienced Machine Learning Team Lead to accelerate our efforts towards exceptional speech solutions. Our Modelling Team builds and trains a broad spectrum of models, including large self-supervised architectures, advancing Speechmatics’ mission to provide the most accurate speech recognition technology worldwide. It also ensures their deployment into production, working with the latest developments in ML, but also with the best practices for software engineering and model serving. What you’ll be doing: <ul class="ak-ul" data-local-id="726917c5-2aea-4fd0-be3f-14de75b13614" data-indent-level="1"> <li> Lead, mentor, and grow a world-class team of ML engineers through coaching and professional development </li> <li> Provide technical oversight and guidance for end-to-end ML development, ensuring solutions are robust and scalable </li> <li> Guide the adoption of best practices in software engineering </li> <li> Plan work, allocate resources and collaborate cross-functionally to align initiatives with business objectives and bring solutions to production </li> <li> Drive the technical work for the ML team to deliver high-quality technical solutions that achieve ambitious strategic goals </li> </ul> Who we are looking for: <ul class="ak-ul" data-local-id="a2898578-b5e7-4c93-9b50-38948be3a11f" data-indent-level="1"> <li> Demonstrable experience in guiding and mentoring ML engineers </li> <li> Adept at prioritizing work by impact, feasibility, and time-to-value, with clear success metrics </li> <li> Strong understanding of the modern Machine Learning stack, for example: - Knowledge of contemporary transformer architectures, related concepts (e.g., GQA, KV-caching) and best practices - Expertise in distributed training techniques - Familiarity with optimisation strategies for model inference (e.g., dynamic batching, flash attention, speculative decoding) </li> <li> Proven track record of developing and deploying models at scale </li> <li> <p data-rend

Skills

Machine Learning

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