Top Machine Learning Jobs in Bristol

Lead the development and deployment of AI systems, designing scalable ML software and infrastructure while mentoring junior engineers and advising clients.

Advise AI-focused customers to design scalable AI solutions and manage large-scale GPU deployments. Deliver technical presentations, documentation, and webinars. Collaborate with engineering and product teams to prioritize and convey customer feedback.

Develop and implement deep-learning and computer-vision models for histology imaging to create spatial biology biomarkers. Collaborate with engineers, clinicians and product teams, build scalable data pipelines, manage regulatory/product inputs to translate research into clinical-grade tools, and communicate results via stakeholders and publications.

Lead enterprise AI strategy and roadmap; build and scale cross-functional AI/ML teams; architect and deliver LLM, RAG, predictive and data-heavy AI products; establish MLOps, governance, cloud-native platforms (Azure/Databricks/AWS/GCP); drive product lifecycle, KPI tracking, security, and responsible AI practices.

The role involves training and evaluating AI models, conducting AI training tasks, and providing expert feedback on their performance using machine learning skills.

AI Trainers will complete tasks involving analyzing, editing, and annotating data to improve AI models with real ML expertise, including performance evaluation and providing technical feedback.

The Senior Applied ML Engineer will build and maintain production ML systems for catalogue decision-making, improving data quality, product categorization, and discoverability.

As a Python and Kubernetes Software Engineer, you will develop data analytics solutions using open source tools, collaborating with a distributed team.

The Senior Machine Learning Engineer will design and implement ML pipelines, optimize LLM performance, and collaborate on AI infrastructure for cost-efficient solutions.

The Machine Learning Engineer will build, refine, and optimize AI/ML prototypes while collaborating with teams on technical documentation, deployment, and model integration.

The Senior ML Data Engineer will enhance data workflows, collaborate with teams to build datasets, influence data strategies, and implement pipelines for effective data usage in AI/ML products.

The Staff Machine Learning Engineer will lead the execution of ML systems, ensuring reliability and scalability while collaborating with cross-functional teams to deliver high-quality AI solutions.

The role involves building end-to-end production-grade ML systems, optimizing pipelines, integrating models into applications, and maintaining data systems.

As Technical Lead, you'll own ML system execution, manage data pipelines, model deployment, and ensure performance under real-world conditions while collaborating with the team.

The VP of Research, Machine Learning will lead the research direction for AI systems, focusing on intelligence design, evaluation frameworks, and advanced machine learning techniques while ensuring real-world applicability.

The Senior Machine Learning Engineer will build core ML systems, handle data preparation and iteration, debug model issues, and ensure reliable production performance while mentoring other engineers.

Lead end-to-end ML initiatives: integrate and fine-tune models, optimize GPU inference for latency and throughput, build evaluation and monitoring tooling, collaborate on scalable serving systems, and mentor engineers.

The AI Director will oversee ML bot development and implementation, identify AI opportunities, educate teams, and prototype AI solutions.