Top Machine Learning Jobs in Bristol

Lead and grow a team of ML scientists to build and productionise credit underwriting, pricing, limits, and lifecycle models. Define Borrowing's ML roadmap for UK growth, new segments, and EU expansion, improve feature engineering and data use, and raise standards for model quality, governance, and iteration speed.

As a Staff/Principal Machine Learning Engineer, you will optimize and scale multimodal inference systems, ensuring high performance and reliability in production environments.

Join Starling as a Software Engineer to design, implement, and maintain ML-powered features, collaborating with engineers and data scientists, while delivering impactful solutions in a hybrid working environment.

As a Machine Learning Engineer, you will develop production-ready ML solutions, collaborate with multidisciplinary teams, and contribute to Defence and National Security challenges.

Build and operate ML infrastructure for training, evaluation, deployment, inference, and experimentation. Improve reliability, scalability, latency, throughput, and cost efficiency. Create reproducible pipelines, observability, benchmarking, and platform tooling enabling researchers and engineers to ship models to production.

As a Lead Machine Learning Engineer, you will set technical direction for ML projects, design scalable systems, mentor engineers, and drive innovation. You'll ensure successful project outcomes while balancing trade-offs and guiding team priorities.

Lead technical development and production deployment of advanced ML solutions (including LLMs, RAG, graph and sequence models) to improve customer operations. Provide hands-on engineering, mentor ML engineers, collaborate with product and backend teams, and drive MLOps and lifecycle strategy to route, disambiguate and resolve customer queries at scale.

Lead technical ML efforts to detect and prevent financial crime by designing, developing and shipping production deep learning, graph and sequence models. Provide IC technical leadership, mentor peers, work with product, engineering and MLOps to iterate models on a cloud-native platform, and measure production impact while minimising false positives and operational costs.

Lead development and deployment of AI systems, design scalable ML software, mentor junior engineers, and collaborate with teams to solve client challenges.