Top Machine Learning Jobs in Bristol

Drive product and business impact through statistical modeling, experiment design, Bayesian marketing analysis, machine learning, NLP, LLM applications, and classifier implementation. Build growth metrics, monitoring pipelines, evaluation datasets, and AI trigger systems across search and Duck.ai. Analyze privacy-constrained data, deploy production-grade models, optimize SQL queries, and lead complex projects from scoping through production with minimal direction.

Software engineering intern supporting the Platform Engines group and Policy Engine for SailPoint’s cloud identity security platform. Responsibilities include developing software, procedures, and test solutions; collaborating with software and QA engineers, technical writers, and product managers; and working in Agile teams. The intern will gain exposure to cloud and software technologies including Golang, Angular, AWS, Docker, Kafka, Kubernetes, REST, SQL, and Temporal.

Supports technical sales for HPC and AI solutions by gathering customer requirements, designing architectures, preparing proposals, delivering demonstrations and proof-of-concepts, and validating solutions. Collaborates with sales teams, customers, partners, and technical specialists to position solutions, manage opportunities, contribute to pipeline growth, and support successful knowledge transfer. The role also involves tracking emerging technologies, participating in forecasts, documenting activities, and developing expertise in enterprise infrastructure and cloud technologies.

Design and deliver enterprise-grade machine learning systems and AI-powered applications for banking. Build scalable, secure, distributed applications and automated cloud, desktop, and ML pipelines. Apply MLOps practices for versioning, reproducibility, and observability while collaborating with cloud and site reliability engineering teams. Develop reusable libraries and business-critical, data-intensive solutions, aligning machine learning problems with business objectives. The role may include mentoring or people leadership.

Develop advanced data science solutions for British Army training, safety, and operational challenges. Responsibilities include preparing and analysing data, applying statistical and machine learning methods, building predictive models and data products, creating reports and dashboards, and communicating insights to technical and non-technical stakeholders. The role also involves collaborating with military and technical teams, mentoring colleagues, and maintaining data governance, ethics, and security standards.

Lead client application and technology strategy reviews, assess architectures, infrastructure, data flows, security, and SDLC practices, and recommend improvements. Develop architecture reports covering risks and best practices. Advise senior stakeholders on cloud, AI, GenAI, and machine learning adoption, challenge technical decisions, support sales proposals, and identify additional client value through credible software and enterprise architecture solutions.

Develop and optimise state-of-the-art machine learning models for specialised hardware and systems containing thousands of accelerators. Responsibilities include model training, benchmarking, profiling, kernel optimisation, experiment design, software testing, code reviews, and collaboration with research, software, and product teams to advance AI hardware and software. The role also involves distributed training and inference, performance analysis, and engagement with the broader AI community.

Leads technical direction for GitLab’s Core DevOps services, covering CI, CD, planning, and source control. Defines architecture, quality metrics, migration strategies, and technical roadmaps; addresses systemic risk and complex design decisions; prototypes and writes code when needed; partners with Infrastructure, Security, SRE, and Product; adopts AI capabilities; mentors senior engineers; supports incident response and senior technical hiring.

Manage strategic Central Government client relationships, identify and shape new business opportunities, lead bids, coordinate multidisciplinary teams, and develop value propositions. The role requires market intelligence, stakeholder engagement, and collaboration with technology, delivery, resourcing, and talent teams. Regular customer meetings across the UK and Bristol office attendance are expected. Candidates must be British passport holders who have lived permanently in the UK for five years and can obtain security clearance.

Provide expert technical advice on age assurance and digital identity technologies within online safety regulation. Lead research and evaluations, assess technical feasibility and risks, translate complex concepts for policy and leadership audiences, prepare briefings and demonstrations, monitor emerging trends, and engage with academia, industry, public bodies, and external technical groups.

Graduate engineers join a rotational programme gaining exposure to software, machine learning, systems, safety, radio frequency, and infrastructure engineering. Responsibilities include integrating sensor and data feeds, designing systems for degraded communications, deploying edge machine learning models, developing deployable sensing and communications products, and supporting scalable edge-device deployment. Graduates work on defence, emergency services, and national security systems with increasing ownership and mentorship.

Supports enterprise sales by discovering customer requirements, mapping them to SailPoint solutions, and delivering foundational implementations and proof-of-value engagements. Acts as a technical lead and consultative advisor, educating customers on architecture and best practices. Collaborates with sales, product, professional services, partners, and customer success teams while developing AI-driven and agentic solutions for demonstrations and customer outcomes.

Support the design and deployment of end-to-end AI solutions, including data pipelines, model training, agentic AI systems, MLOps, monitoring, and system integration. Contribute to architecture documentation, technical proposals, tooling assessments, risk analysis, and customer engagements while developing solution architecture expertise under senior mentorship. The role requires AI or ML engineering experience, clear communication, awareness of AI security, and eligibility for UK SC clearance.

Lead the architecture, governance, and delivery of enterprise AI, machine learning, generative AI, RAG, and agentic systems on GCP. Define solution strategies, architecture standards, security controls, and technical direction while delivering scalable, production-ready platforms. Responsibilities include designing LLM and multi-agent applications, cloud-native AI systems, MLOps and LLMOps processes, enterprise integrations, monitoring, evaluation, and stakeholder collaboration.

Leads the development of AI-powered developer productivity tools and shared ML capabilities across the software development lifecycle. Responsibilities include evaluating AI tooling, building RAG and agent systems, implementing MCP integrations, developing ML CI/CD infrastructure, defining productivity metrics, ensuring AI governance and security compliance, creating reusable prompts, and training engineering teams. The role requires hands-on technical leadership, strong MLOps expertise, production ML experience, and the ability to drive adoption across distributed teams.

Leads strategy and execution for pricing, driver matching, and incentive products supporting delivery operations across international markets. Owns product vision, roadmaps, discovery, experimentation, analytics, launch execution, and value realization. Partners with Engineering, Design, Analytics, and business stakeholders to optimize driver experience, delivery performance, and cost per order. Influences technical direction, evaluates AI opportunities, mentors product managers, and addresses local regulatory and market requirements in Canada and Latin America.

Lead the vision, strategy, roadmap, and lifecycle of data, analytics, and AI-enabled products. Identify commercial opportunities, develop pricing and business cases, manage budgets, and drive product profitability. Partner with Product, Data Science, Engineering, and Commercial teams to deliver scalable solutions. Lead and develop Data Product Managers while influencing senior stakeholders and translating technical capabilities into customer and business value.

Own the market strategy and growth of Luma’s lab automation and orchestration solutions. Engage pharma, biotech, CDMO, and research customers; define segments, personas, positioning, competitive strategy, pricing inputs, and AI use cases. Enable global sales and marketing teams with messaging and sales tools, align Siemens organizations, support strategic pursuits, and build partnerships with automation vendors and thought leaders. Drive software adoption, pipeline growth, and customer movement toward connected and autonomous laboratories.

Lead and mentor a data science team developing machine learning systems for threat detection and agentic AI. Set ML technical strategy, standards, roadmaps, and deployment practices; guide projects from research through production; collaborate with security, engineering, and product teams; develop advanced malware and behavioral threat models; communicate insights; and contribute to research, publications, patents, and hands-on technical work.

Translate ambiguous business questions into actionable insights using Python, SQL, statistics, forecasting, regression, classification, segmentation, and machine learning. Analyze complex datasets, validate findings, build models, and communicate commercial implications to senior stakeholders. The role requires strong requirements gathering, stakeholder management, written communication, and the ability to turn analysis into practical decisions in a financial services environment.

The role involves designing scalable data pipelines, implementing custom ML algorithms, and working on microservice architectures for ad optimization models.

Lead real-world evidence biostatistical analyses using large-scale healthcare data to support clinical development, regulatory strategy, HEOR, and payer decisions. Design observational studies and target trial emulations, apply causal inference, statistical learning, machine learning, and NLP, and build reproducible Python/R workflows. Collaborate with clinical, epidemiology, data engineering, and HEOR teams while translating findings for technical and non-technical stakeholders. Support regulatory submissions, governance, audits, and scientific dissemination.

Leads biomedical data science for clinical and health research, integrating clinical, observational, EHR, genetic, wearable, electrophysiological, and video data. Develops signal-processing methods, machine-learning models, probabilistic and time-series analyses, scalable data pipelines, and reproducible workflows. Validates clinically relevant models, supports disease phenotyping and predictive modeling, ensures compliance with pharmaceutical and healthcare standards, and communicates technical findings to clients and stakeholders.

Leads the design, development, deployment, and governance of scalable Data and AI solutions using Databricks, Microsoft Foundry, and Azure. Defines AI technology roadmaps, mentors engineering teams, identifies high-value business use cases, prototypes and scales AI products, and ensures secure, ethical, enterprise-integrated implementations.

As a Principal Engineer at M&S, you'll drive technical strategy, influence architecture, and mentor engineers while delivering on personalization, search, and machine learning features in collaboration with cross-functional teams.