Top Machine Learning Jobs in Bristol

Lead platform product ownership bridging cloud and firmware strategy and execution. Manage and prioritize backlog, engage customers, collaborate with R&D, define releases, and drive agile delivery aligned with product leadership.

Own the Device Intelligence product roadmap for tier‑1 network service providers. Define PRDs, AI/LLM-assisted workflows, and technical requirements; lead discovery, backlog prioritization, and cross-functional delivery; engage strategic customers and support sales with competitive positioning.

The Principal Solutions Architect leads technical solution design, partners with hyperscalers, ensures architectural integrity, and drives customer engagement through compelling narratives and demos.

The Data Scientist will analyze conversational data to provide insights, enhance AI capabilities, and deliver value for Fortune 500 clients.

As a Machine Learning Engineer, you will design and enhance models for conversational AI, addressing challenges in NLP and enterprise deployment.

As an AI Trainer - Machine Learning Specialist, you will provide expert feedback on AI models, complete AI training tasks, and assess AI performance. Candidates must exhibit ML expertise and complete a skills verification test.

As a C++ Engineer, you will develop machine learning features, integrate models, maintain SDKs, and collaborate across teams for software performance and enhancements.

As Senior Product Manager, you'll lead the integration of global airline partners, optimize supply metrics, and enhance product specifications for seamless booking automation, focusing on AI solutions and API performance.

Drive independent, high-impact research on frontier AI topics, publish findings, collaborate widely, and mentor new researchers in the field.

Drive data-informed capital allocation by building financial models (NPV, IRR), applying AI/ML for forecasting and scenario planning, creating build-vs-buy and prioritization frameworks, implementing decision-support systems, collaborating with cross-functional stakeholders, and monitoring post-investment performance to refine allocation models.

Contract role combining actuarial science, risk management, and AI to improve portfolio performance. Responsibilities include loss and combined ratio analysis, portfolio risk assessment, catastrophe and exposure modeling, stress testing, and integrating ML to enhance pricing, reserving, and risk selection while collaborating with underwriting, finance, and product teams.

Design, implement, and monitor AI/ML models to improve underwriting risk assessment, pricing, and portfolio management. Translate underwriting rules into data models, integrate models into workflows, analyze datasets for risk patterns, build decision-support tools, ensure compliance and fairness, and collaborate with underwriting, actuarial, product, and engineering teams.

Design, implement, and optimize AI-powered solutions to support financial compliance and controls. Monitor GAAP/IFRS adherence, automate controls and reporting, support internal/external audits, and manage complex accounting areas like revenue recognition and lease accounting while collaborating with finance, audit, and technology teams.

The Customer Success Manager will onboard and support Portuguese utility accounts, ensuring effective use of digital twin technology and driving long-term partnerships and customer satisfaction.

The Data Scientist will analyze data, build and refine machine learning models, document technical designs, and collaborate with multidisciplinary teams to deliver AI solutions.

The Executive Partner manages a portfolio of clients, providing expert guidance in Data & Analytics, mentoring executives, and ensuring client value through various engagements. Responsible for retention and growth, the role emphasizes collaboration with internal teams and requires strong executive presence.

Lead cross-functional teams of machine learning engineers and data scientists, driving technical direction and fostering a collaborative environment to develop high-quality ML-driven capabilities.

Own end-to-end ML projects focused on recommendation and LTV signals: develop, evaluate, and deploy models; build training/scoring pipelines; run A/B tests; monitor production models; collaborate with product and analytics; mentor colleagues.

The role involves analyzing biological mass spectrometry data, developing software tools, and scaling workflows for AI and human consumption in drug safety assessment.

This role involves leading the development of AI systems to replace lab toxicity experiments, focusing on end-to-end ML architecture, research on chemistry-biology correlations, and building innovative models. Candidates should have strong ML skills, engineering abilities in Pytorch and Python, and a passion for both science and technology.

Design, train, evaluate, and deploy predictive transportation and delivery ML models (primarily XGBoost). Perform EDA, feature engineering, experiment design, and statistical evaluation. Build production ML pipelines and MLOps practices on Google Vertex AI. Collaborate with data engineering and supply chain stakeholders to translate operational needs into scalable solutions.

Lead and own the ML function end-to-end for Rosso: set ML strategy, hire and develop ML engineers, build operating systems (cadences, monitoring, incident review), drive ML-software collaboration, and be accountable for ML outcomes across forecasting, pricing, optimisation, and classical ML.

Lead pricing ML for Rosso: define technical direction, build production-grade price-optimisation and probabilistic imbalance models, bridge modelling and MLOps, and communicate model decisions across teams.

Lead and own tem's ML function (Rosso): set ML strategy, hire and develop ML engineers, establish operating systems (monitoring, incident reviews, prioritisation), and drive close collaboration with software engineering to deliver forecasting, pricing, and optimisation models that meet business outcomes.

The Senior Product Manager will set product direction, own the development lifecycle, collaborate with teams, and ensure high-quality outputs. This role requires strong opinions on product quality and customer insights.