Top Machine Learning Jobs in Bristol

Lead the engineering strategy and execution for an AI-powered email application across backend, mobile, and desktop platforms. Design reliable AI workflows involving orchestration, state, tool use, retries, fallbacks, and failure handling. Build APIs connecting AI capabilities to product experiences, lead a senior engineering team, make architecture and scalability decisions, and establish strong observability, monitoring, testing, security, and operational foundations. Partner with ML leadership to deliver fast, reliable, maintainable product experiences.

Lead application engineering for an AI email product across backend, mobile, and desktop platforms. Design reliable AI workflows involving orchestration, state, tool use, retries, fallbacks, and failure handling. Build APIs connecting AI capabilities to product experiences, guide a senior engineering team, make architecture and reliability trade-offs, and establish observability, monitoring, testing, and operational excellence. Partner with ML leadership to ship scalable, maintainable, and high-performing AI-powered experiences.

Own the end-to-end product direction and quality of AI-powered systems. Define requirements, evaluation frameworks, quality bars, and feedback loops while balancing model quality, latency, cost, reliability, safety, and user experience. Partner deeply with ML, backend, and client engineers on system design and iteration, identify failure modes, and drive execution in a fast-moving zero-to-one environment.

Leads application engineering for an AI-powered email product across backend, mobile, and desktop platforms. Designs reliable AI workflows involving orchestration, state, tool use, retries, fallbacks, and failure handling. Builds APIs connecting AI capabilities to product experiences, makes architectural trade-offs, establishes observability and operational excellence, and leads a small senior engineering team through mentorship, design reviews, and code reviews. Partners with ML leadership to deliver scalable, reliable, and maintainable production systems.

Lead application engineering for an AI-powered email product across backend, mobile, and desktop platforms. Design reliable AI workflows involving orchestration, state, tool use, retries, fallbacks, and failure handling. Build APIs connecting AI systems to product experiences, make architecture and scalability decisions, mentor senior engineers, and establish strong observability, monitoring, testing, security, and operational practices. Partner with ML leadership to ship fast, reliable, maintainable product experiences.

Lead developer relations for NVIDIA technologies across the EMEA energy and utilities ecosystem. Build strategic relationships with power, utility, grid, simulation, digital twin, and renewable energy software providers. Guide partner onboarding, integration, technical enablement, and co-innovation while identifying growth opportunities and shaping adoption strategies. Collaborate with engineering, product, marketing, and solution architecture teams; communicate technical concepts to diverse audiences; and advocate for partner feedback to influence product roadmaps.

Develop and validate agentic AI capabilities for patent analysis using machine learning, NLP, LLMs, RAG, hybrid search, and agentic workflows. Analyze large-scale intellectual property datasets, design experiments, evaluate and benchmark models, define reasoning strategies, and translate findings into product recommendations. Collaborate with product managers, IP experts, and engineers to deliver scalable, customer-facing solutions.

Develop and validate agentic AI capabilities for patent analysis using machine learning, NLP, LLMs, generative AI, retrieval, and hybrid search. Define multi-step reasoning workflows, analyze large-scale intellectual property datasets, establish model evaluation and benchmarking practices, and translate experiments into product recommendations. Collaborate with product teams, intellectual property experts, and engineers to deliver scalable, customer-facing solutions.

Leads complex machine learning delivery and Defence-sector strategy, translating client challenges into technical roadmaps, scoping projects, managing client relationships, and supporting technical sales. Writes production-grade Python, serves as a machine learning authority, develops Generative AI and agentic AI solutions using LLMs, and mentors and manages data scientists.

Lead and scale engineering teams building multi-tenant Voice AI, Agent QA, analytics, auto-learning, and workforce management platforms. Own architecture for high-throughput voice data systems, real-time pipelines, telephony integrations, optimization algorithms, and enterprise compliance. Recruit and develop senior engineers and managers, establish engineering standards, guide cross-functional roadmap alignment, and deliver reliable production systems across global geographies.

Join a hybrid Bristol-based engineering team designing, integrating, and delivering robust systems for degraded or denied operational environments. Responsibilities include translating user needs and CONOPS into requirements, applying systems engineering lifecycles, leading integration and architecture tasks, assessing trade-offs and risks, ensuring safety/security compliance, and mentoring junior engineers. SC clearance eligibility required.

Design and deploy machine learning models for financial document processing, optimize large language models, collaborate with cross-functional teams, and guide junior peers in a fast-paced environment.

Work with UWE Bristol and Viper Innovations to develop advanced analytics and machine learning algorithms for electrical distribution systems. Build data pipelines, manipulate large datasets, design and test experimental methods, and deliver product features that extend operational life of control and distribution equipment. 24-month KTP role with training and mentorship.

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.

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.

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.

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.

The role involves building infrastructure for training open source foundation models, with emphasis on autonomy and contributing in a dynamic team.

As a P&C Actuary, you will assess insurance risks, review regulatory compliance, support audit teams, and document findings while nurturing client relationships and contributing to practice development efforts.

Leads global AI quality, accreditation, and conformity assessment activities, including obtaining EU AI Notified Body designation, developing regulatory processes, and ensuring consistent certification practices. The role advises internal teams and customers, represents BSI in international technical forums, establishes competency criteria, supports regulatory programs, identifies process improvements, and shapes the AI and machine learning business strategy. It requires expertise in global AI regulations, quality management, legislation, technical product areas, stakeholder influence, and people leadership.

The role involves designing, building, and deploying ML systems, supporting stakeholders, and operationalizing AI applications in Defence.

Develop and deploy machine learning and statistical models that solve business problems and create measurable impact. Responsibilities include exploratory data analysis, feature engineering, experimentation, model optimization, explainability, MLOps, prompt engineering, and cloud-based data collaboration. The role involves working with clients and stakeholders, communicating insights through visualization and storytelling, and contributing to scalable analytics, Generative AI, and data science solutions.

Own the full business development and sales cycle across Europe, including prospecting, partnerships, stakeholder engagement, negotiation, contracting, and closing against revenue targets. Develop channel opportunities, support European go-to-market strategies, communicate customer ROI, attend industry events, and collaborate with Marketing, Product, technology partners, and agencies. Track funnel performance, improve acquisition processes, and share market insights while engaging apparel brands and retailers.

Provide technical leadership for solution design and assurance across pre-sales and delivery. Translate ambiguous customer needs into costed, deliverable architectures for sensing, AI, communications and edge environments; assess hosting, integration and platform options; manage technical risks; produce bid and governance artefacts; engage stakeholders and support contract mobilisation to ensure coherent end-to-end delivery.

Lead technical development and production deployment of ML models for search, personalization, ranking, and recommendations. Build scalable, explainable ML solutions, own end-to-end impact, collaborate with backend and product teams, and iterate on experiments using cloud-native data at scale.