Machine Learning Engineer

• Build, improve and maintain machine learning pipelines used within Market Pricing
• Productionise Python-based models, notebooks and data science workflows
• Move models from development into repeatable, tested and documented production processes
• Improve testing, release, monitoring and maintenance of model workflows
• Investigate issues across data, model and pipeline workflows
• Automate manual steps to improve quality, speed or control
• Support Git, code review, testing and documentation practices
• Ensure pipelines and model workflows are traceable, auditable and safe to change
• Collaborate with Pricing, Data Science, Data Engineering, ML Engineering and governance stakeholders
• Own defined MLOps components and pipelines
• Work with senior colleagues on standards, architecture and governance

MLOps Engineer productionising machine-learning workflows for Hastings Direct’s UK digital insurance business

MLOps Engineer

MLOps Engineer productionising Python and SQL machine-learning workflows for Hastings Direct, a UK digital insurance provider. Building reliable, governed pipelines for Market Pricing.

AI Engineer

• Report to the Director of Technology AI
• Contribute as a key member of the Advanced AI team, Digital Catapult’s core AI research unit
• Lead original AI/ML research at Technology Readiness Level 3 and above
• Develop intellectual property and organisational capabilities in cutting-edge AI technologies
• Build academic and industry partnerships bridging early-stage research and industrial application
• Develop industrial R&D funding proposals for Innovate UK, EPSRC, ARIA and Horizon Europe
• Lead delivery of R&D projects
• Contribute to the AAIT AI research strategy, AI Technology Roadmap and Radar
• Own and define a research area and plan aligned with other Digital Catapult teams
• Mentor junior team members in AI and operations
• Participate in open innovation activities as an AI/ML expert
• Provide technical consulting to startups and corporates
• Act as an AI thought leader and source of expertise across the organisation

Lead AI/ML research and industry R&D at UK innovation centre Digital Catapult

Lead AI/ML Technologist

Lead AI/ML research, intellectual property and funded R&D projects at Digital Catapult. Building academic and industry partnerships across agentic, neuro-symbolic and advanced machine learning.

• Own and advance a predictive modelling platform that scales across problem types and tenants
• Design, implement, and iterate models including embedding-based sequence encoders, temporal survival models, and gradient-boosted decision trees
• Predict key vulnerabilities in housing, health, and other social domains
• Track developments in ML and frontier models and run structured experiments to bring promising techniques safely into production
• Build robust evaluation pipelines, training datasets, and model infrastructure
• Support continuous improvement of natural language and predictive analytics
• Ensure responsible AI deployment by embedding ethical and regulatory considerations throughout development
• Work in the Platform team responsible for deployment and evolution of the backend platform underpinning Xantura’s core business

Machine Learning Engineer advancing Xantura’s social-domain predictive analytics platform

Machine Learning Engineer advancing Xantura’s predictive modelling platform for housing, health, and social vulnerabilities. Deploying responsible ML systems, pipelines, APIs, and models to production.

• Build, improve and maintain machine learning pipelines used within Market Pricing
• Productionise Python-based models, notebooks and data science workflows
• Move models from development into repeatable, tested and documented production processes
• Improve testing, release, monitoring and maintenance of model workflows
• Investigate issues across data, model and pipeline workflows
• Automate manual steps to improve quality, speed or control
• Support Git, code review, testing and documentation practices
• Ensure pipelines and model workflows are traceable, auditable and safe to change
• Collaborate with Pricing, Data Science, Data Engineering, ML Engineering and governance stakeholders
• Own defined MLOps components and pipelines while contributing to standards, architecture and governance

MLOps Engineer building production machine-learning pipelines for UK digital insurer Hastings Direct

MLOps Engineer productionising machine-learning models and pipelines for Hastings Direct, a UK digital insurance provider.
Improving testing, deployment, monitoring and governance across pricing workflows.

• Lead the design and development of a next-generation AI platform
• Build sophisticated multi-agent AI solutions for real-world business challenges
• Own the AI layer of the platform and design robust, scalable, production-ready AI systems
• Prioritise quality, reliability and security of AI systems
• Work alongside backend engineers, architects and product teams
• Deliver innovative AI capabilities and influence the technical direction of the AI strategy
• Develop AI-powered applications using TypeScript, Golang and Python
• Design and implement multi-agent AI architectures
• Build automated LLM evaluation and regression testing frameworks
• Design agent memory and context management strategies
• Implement AI safety, guardrails, prompt-injection mitigation, observability and LLM telemetry
• Coach and mentor colleagues
• Contribute to the development of business scenarios

Senior AI Engineer building multi-agent AI platforms for Spyrosoft, a software engineering company

Senior AI Engineer

Senior AI Engineer building multi-agent AI platforms for Spyrosoft, a software engineering company. Designing secure, observable LLM systems with TypeScript, Go and Python.

• Design and build core primitives for agentic systems, including memory, context management, tool-calling, orchestration, and feedback loops
• Develop reusable agentic infrastructure to introduce new workflows quickly and reliably
• Build evaluation and observability frameworks for testing, tracing, debugging, and assessing AI agents before production deployment
• Create coding-agent and specialized-subagent workflows that explore repositories, implement changes, review architecture, run evaluations, and iterate with minimal human coordination
• Design systems integrating specialized agents, tools, deterministic software, and humans
• Decide when to use LLMs, traditional software, human intervention, or coordinated agents
• Partner with operational and product teams to understand acquired-agency workflows and identify automation opportunities
• Turn complex operational processes into reliable production systems
• Define architectural patterns, tooling, and engineering standards for agentic systems
• Deliver first-year outcomes in reliability, workflow-integration speed, operational efficiency, and engineering leverage

Applied AI Engineer building agentic automation infrastructure for Dwelly’s residential lettings platform

Applied AI Engineer

Applied AI Engineer building reliable agentic infrastructure for Dwelly, an AI-powered residential lettings operator. Automating agency workflows through LLM orchestration, evaluation, observability, and production systems.

AI Research Scientist

• Conduct foundational pre-training for LLMs and multi-modal models integrating text, vision, audio, or other modalities on large distributed servers with multiple nodes and thousands of NVIDIA GPUs
• Design, prototype, and scale innovative architectures, tokenizers, and cross-modal alignment layers
• Source, filter, and curate large-scale textual and multi-modal datasets and establish robust data pipelines
• Execute experiments independently and collaboratively, analyze results, and refine training methodologies for performance and token efficiency
• Investigate, debug, and eliminate bottlenecks in model efficiency, computational performance, and multi-modal alignment stability during long training runs
• Advance distributed training systems for scalability and hardware efficiency

AI Research Engineer advancing LLM and multimodal pre-training at blockchain fintech Tether

AI Research Engineer – Pre-training, LLM, Multi-Modal

AI Research Engineer advancing LLM and multimodal pre-training for Tether’s blockchain-powered digital finance and AI products. Designing architectures, curating datasets, and scaling distributed GPU training.

• Design, implement, and optimize machine learning and computer vision models for product capabilities
• Research, evaluate, and apply CNNs, transformers, and vision-language models
• Implement and benchmark algorithms on large-scale datasets for accuracy and throughput
• Fine-tune large-scale models using LoRA and QLoRA
• Define, implement, and monitor evaluation metrics including precision, recall, ROC-AUC, and confusion matrices
• Analyze training, test, and production data to identify performance gaps and reliability risks
• Improve model accuracy, robustness, and system stability through data-driven enhancements
• Support end-to-end ML workflows spanning data preparation, training, deployment, monitoring, and iteration
• Contribute to CI/CD pipelines and production monitoring for reliable, reproducible, scalable model delivery
• Diagnose and resolve model performance regressions and production issues
• Mentor junior CVML engineers across ML project phases
• Participate in design reviews, technical discussions, knowledge sharing, and Agile ceremonies
• Suggest improvements to models, workflows, tools, and product features
• Collaborate with engineering, product, and data stakeholders
• Monitor emerging ML and computer vision trends and assess their real-world applicability

Senior ML Engineer building production computer-vision models for GBG’s digital identity verification technology

Senior Machine Learning Engineer

Senior ML Engineer developing production computer-vision models for GBG’s digital identity verification technology.
Deploying, evaluating, and improving AI systems while mentoring CVML engineers in an Agile environment.

• Own the organic and AI search performance for a flagship UK automotive account with over 20 franchise websites
• Create and execute SEO and AI Search strategy
• Prioritise strategy, deliver commercial outcomes, ensure quality assurance, and drive innovation
• Serve as the primary senior client contact
• Lead weekly update meetings and maintain regular email contact
• Own quarterly business reviews and present complex strategy, technical recommendations, and performance reporting to senior stakeholders
• Direct the day-to-day workload of the SEO and AI Search Executive
• Allocate tasks and quality-check output
• Monitor search algorithm updates, generative AI in search, and automotive market trends
• Apply current knowledge to keep account strategy ahead of the market

Lead SEO and AI search strategy for Keyloop’s flagship UK automotive franchise account

Lead SEO and AI Search Strategist

Lead SEO and AI search strategy for Keyloop, a technology platform connecting automotive dealers, manufacturers, suppliers, and buyers.
Managing a flagship UK franchise account, senior client relationships, performance, and SEO team direction.

• Lead the design, quantization, and deployment of edge-native AI models and knowledge analytics engines
• Transition Small Language Models and knowledge graph pipelines into air-gapped, degraded, and bandwidth-constrained tactical hardware
• Deploy deterministic, explainable, human-in-the-loop decision-support tools for intelligence and operational users
• Quantize, fine-tune, and optimize open-source SLMs and vision-language models for low-power edge runtimes
• Design and maintain lightweight on-device graph databases and relationship extraction pipelines for structured and unstructured sensor data
• Implement bounding guardrails, prompt evaluation, and anti-hallucination controls
• Build reproducible model training, evaluation, and containerised deployment pipelines for air-gapped or low-bandwidth environments
• Translate complex ML/AI concepts into technical recommendations for MoD stakeholders, DSTL assessors, and Prime contractors

Senior Machine Learning Engineer deploying dependable edge AI for UK defence projects at Zaizi

Senior Machine Learning Engineer deploying quantized SLMs and knowledge graphs on constrained edge hardware. Ensuring dependable, explainable AI for UK defence and intelligence users at Zaizi.

• Write production-quality code every day across the full stack, covering backend, front-end, and the infrastructure that supports it.
• Take ownership of features and workstreams from design through to deployment, delivering working software in shippable increments.
• Contribute to technical design and architecture discussions, bringing well-reasoned input and a practical perspective.
• Run technical spikes and proofs-of-concept to explore approaches and reduce uncertainty before committing to a direction.
• Manage your own technical debt responsibly, keeping the codebase in a state you are proud of.
• Play an active part in the decomposition of shared services into team-owned components, taking on defined pieces of that work and delivering them with care.
• Support the move to CI/CD by contributing to pipeline work, writing automated tests, and adopting trunk-based development as the default way of working.
• Build with observability in mind from the start, covering instrumentation, logging, and monitoring so that your team can see what their systems are doing in production.
• Contribute to the team's growing DevOps capability, taking on operational responsibility for the systems you build.
• Write clean, testable, secure code and hold that standard consistently, not just when it is convenient.
• Practise test-driven development and contribute to a culture where good testing is the norm, using AI to generate scaffolding and explore edge cases as part of that workflow.
• Participate actively in code review, giving and receiving feedback that improves the work and raises the bar for the whole team.
• Apply secure coding practices as a matter of course: dependency management, threat awareness, and security testing.
• Use AI natively in your engineering work, as part of how you think through problems, generate and review code, and build and test solutions.
• Contribute to AI-powered workflows that improve the team's delivery capability, whether that is in automation, testing, code generation, or observability.
• Engage actively with the team's AI agents and tooling, helping to refine and improve them as your understanding of what works deepens.
• Bring a critical eye to AI-generated output, verifying quality and maintaining the standards you would apply to anything else you ship.
• Work closely with your Tech Lead and teammates through pairing, mob programming, and shared problem-solving.
• Support less experienced engineers where you can, sharing knowledge through code review, pairing, and day-to-day conversation.
• Contribute to planning, refinement, stand-ups, reviews, and retrospectives as an engaged and thoughtful team member.
• Take an active interest in your own development, staying current with the technologies and practices the team uses and bringing new ideas back to the team.

AI-Native Software Engineer transforming platforms and building innovative software at Citation Group

AI-Native Software Engineer

AI-Native Software Engineer transforming platform delivery at Citation Group. Collaborating with teams to develop quality software solutions across multiple domains and technologies.

• Lead discovery interviews with business leaders and functional teams.
• Turn business needs into clear learning goals, realistic case studies and practical exercises.
• Design and lead engaging full day AI workshops for groups of up to 60 people.
• Facilitate mixed seniority audiences, from operational staff to senior leaders.
• Explain generative AI, prompting, output checking and responsible use in simple language.
• Coordinate specialist facilitators and keep delivery consistent across the team.
• Review slides, demonstrations, videos, workbooks and facilitator guides before delivery.
• Manage live questions, difficult discussions and changes in the room.
• Capture feedback and recommend practical follow up actions that support adoption.

Lead practical AI learning and adoption programs for organizations at Intelance

Senior AI Learning and Adoption Lead

Senior AI Learning and Adoption Lead at Intelance, facilitating AI workshops and learning experiences for organizations. Requires deep expertise in AI and substantial consulting experience.

• Sit with the teams who feel the pain, find where an agent would genuinely pay for itself, and get something in front of them quickly enough to learn whether you were right.
• Choose the approach and own the reasoning: low-code, pro-code, or something bought off the shelf, weighed on cost, control and how fast it can land.
• Turn the experiments that earn it into agents we're happy to put in front of players, tested and validated to the standard a live product demands.
• Design for the failure cases: a call that times out, a tool that errors, a job that runs for an hour, a decision the agent should hand back to a person.
• Build the shared frameworks, templates and infrastructure that let other engineers ship their own agents without starting from scratch.
• Set the technical shape of our agent estate: how we retrieve, how we evaluate, how we constrain behaviour, how we see what's happening, and how any of it reaches production.
• Decide what needs sign-off before an agent goes live, how it handles player data, and where its authority stops.
• Leave a clear trail behind every agent: what it does, why we promoted it, what it costs, and whether the return still justifies it.
• Be Stakemate's AI champion: office hours, demos and short training sessions that make the wider team genuinely better at using AI.
• Partner with department leads on where AI helps, and where it doesn't.

Senior AI Engineer developing production-grade AI capabilities at Stakemate.

Senior AI Engineer responsible for bringing AI solutions to production and driving innovation at Stakemate, a fast-growing sports betting startup in London.

• Own activation and platform success
• Deeply understand each customer's specific use cases, business objectives, and success metrics
• Build and co-develop custom AI solutions with customers, translating their specific use cases into workflows that drive measurable business impact
• Design and execute activation strategies that drive measurable platform adoption and business outcomes
• Implement champion programs, office hours, usage dashboards, customized trainings, and workshops
• Create advocate networks and champion communities within customer organizations
• Establish power user communities that drive peer-to-peer learning
• Train internal customer trainers to scale enablement
• Design innovative customer experiences that deepen engagement
• Facilitate dynamic live and virtual training sessions tailored to diverse audiences
• Partner with curriculum team while owning customization and delivery for your customers
• Create memorable learning experiences that inspire action and drive adoption
• Refine materials based on customer feedback and adoption data
• Work cross-functionally to understand evolving customer needs
• Identify gaps and champion new training opportunities
• Build deep relationships with customer stakeholders and executive sponsors

Customer success professional driving AI adoption for enterprise clients

Enterprise AI Adoption Lead

Customer success professional driving deployment and activation of AI solutions for enterprise clients. Collaborating with customers to build successful AI programs and measurable outcomes.

• You’ll work directly with the AI Growth Infrastructure Lead, the Growth Workflow Specialists across channels, and the domain experts who spec the workflows you build.
• You’ll operate within the Growth Foundations Group — the centralized team that builds the engine every product team in the business runs on.
• As AI Engineer, Growth, you will be the second builder on this engine. Working alongside our lead architect, you execute — building, shipping, and iterating the agent orchestrations, automated workflows, and system integrations that power every growth work-stream: paid growth, lifecycle, organic search, CRO, incentives, referrals, and beyond.

Senior AI Engineer building AI-native growth engine at Kraken

Senior AI Engineer – Growth

Senior AI Engineer helping build an AI-native growth engine at Kraken. Collaborating with cross-functional teams to automate workflows and drive growth.

LLM Engineer

• You will own, build, and run your startup in fields such as AI Infrastructure
• You will embark on an extensive personal development journey crafted by unicorn founders and follow a fully customised programme enhancing your goal, time, and energy management
• You will receive support in hiring through our network to over 50,000 professionals and advice as well as best practices from serial entrepreneurs
• You will receive intensive coaching to make your startup ready to raise millions in funding
• You will iterate your product with us until having reached product-market-fit and receive support in building up a sales force or creating a marketing engine respectively

AI Infrastructure Co-Founder / CSO managing startups supported by unicorn founders

AI Infrastructure Co-Founder, CSO

AI Infrastructure Co-Founder / CSO managing startups in collaboration with unicorn founders. Building transformative tech with potential for high revenue growth and investor engagement.

• Partner with engineering leadership to define AI-enablement priorities.
• Work with the team building Aperture by Tailscale.
• Build, maintain, and iterate on internal tools and workflows.
• Evaluate new AI models and make recommendations.
• Collaborate with teams to find opportunities to apply AI.
• Develop infrastructure and practices for safe use of AI-generated code.
• Advocate for effective AI practices through documentation and coaching.
• Track adoption and measure the impact of AI tooling.
• Expect varied tasks including building, teaching, and prioritization.

Software Engineer, AI Enablement developing tools for Tailscale's engineering organization.

Software Engineer, AI Enablement

Join Tailscale as a Software Engineer, AI Enablement. Help build tools and workflows for efficient AI-assisted development.

• LLM Extraction Agents: Design, build, and maintain production-grade LLM-based extraction pipelines to automatically parse years of unstructured PDF pathology reports.
• Structured Parsing: Programmatically extract clinical entities such as diagnoses, tumor grades, pathological staging, and critical biomarker statuses from raw, free-text documents.
• Framework Selection: Evaluate and integrate specialized agentic frameworks and orchestration tooling (e.g., LangChain, LlamaIndex, or direct LLM API implementations) based on measurable extraction accuracy against real-world clinical text, rather than what is fashionable.
• Confidence Scoring & Human-Review Loops: Build programmatic confidence scoring systems and human-in-the-loop validation queues that flag low-confidence extractions for clinical review based on validation parameters defined by our Clinical Informatics Lead.
• Diagnostic Data Fusion: Architect and maintain the data pipelines that link pathology LIS data with separate molecular/genetics information systems. You will ensure that vital markers like KRAS, NRAS, BRAF, MMR/MSI status, and ctDNA results seamlessly map to the exact same case record as the histology diagnosis.
• Interoperable Interface Engineering: Implement robust REST APIs, HL7 v2, or HL7 FHIR interfaces to feed structured pipelines directly into downstream matching layers or ecosystems like Proscia Concentriq and Aperture.
• Future Ecosystem APIs: Lay the architectural groundwork for secure, high-throughput API layers destined to interface with premium consumer wearables, external preventive health apps, and cloud-native hospital systems.
• Data Quality Observability: Develop automated data-quality monitoring systems to catch and flag anomalous outputs, missing biomarker fields, or incomplete clinical records before they touch delivery endpoints.
• Anonymization Infrastructure: Implement technical de-identification protocols to securely strip or pseudonymize direct and indirect patient identifiers.
• Regulatory Alignment: Technical execution must align completely with strict health data privacy guardrails across global and regional frameworks, including the Swiss nDSG and EU GDPR Article 9.
• Lineage Tracking: Build exhaustive audit logging and data lineage tracking for every clinical record processed, preserving clinical data provenance for pharma and clinical partner credibility.

AI Native Engineer designing and deploying AI systems for a leading diagnostics company

AI Native Engineer

AI Native Engineer designing and deploying production-grade AI systems transforming clinical insights for a leading diagnostics organization. Join Unilabs shaping the future of precision medicine.

• Build and ship Android features using Kotlin.
• Create clean mobile flows for insurance, payments, claims, renewals and financial services.
• Work with product and design to simplify complex user journeys.
• Integrate backend APIs and ensure mobile flows are stable, secure and reliable.
• Improve app performance, crash rate, loading states, responsiveness and memory usage.
• Use analytics, user behaviour and production issues to improve the product.
• Build AI-assisted mobile experiences only where they genuinely improve the user journey.

Mobile Application Developer building native Android experiences for BJAK's AI Finance Agent

Mobile Application Developer – AI Finance Agent

Mobile Application Developer building native Android features for BJAK's AI Finance Agent. Focusing on clean UX, performance, and reliable mobile services in a remote UK position.

• Lead the full delivery of AI and machine learning projects, ensuring timelines, budgets, and quality standards are achieved.
• Run and mentor delivery teams, providing clear direction, support, and professional development opportunities.
• Develop and maintain comprehensive project plans, risk registers, and delivery roadmaps using appropriate methodologies.
• Engage with senior collaborators and clients to understand requirements, lead expectations, and communicate progress and outcomes.
• Analyse project performance metrics and implement continuous improvement initiatives to improve delivery efficiency.
• Coordinate prioritisation, capacity planning, and team composition to optimise project outcomes.
• Identify and mitigate risks proactively, developing contingency plans and protocols for blocking issues.
• Champion guidelines in AI delivery, governance, and ethical implementation across the organisation.
• Collaborate with technical teams to ensure architectural decisions align with business objectives and delivery constraints.
• Facilitate knowledge sharing and capability building within the delivery function.

AI Delivery Lead managing AI/ML projects for a global life sciences leader

AI Delivery Lead

AI Delivery Lead overseeing the delivery of AI and machine learning projects at life sciences company. Leading teams to achieve project goals and ensuring alignment with business objectives.

Machine Learning Engineer jobs in United Kingdom

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MLOps Engineer

Lead AI/ML Technologist

Machine Learning Engineer

MLOps Engineer

Senior AI Engineer

Applied AI Engineer

AI Research Engineer – Pre-training, LLM, Multi-Modal

Senior Machine Learning Engineer

Lead SEO and AI Search Strategist

Senior Machine Learning Engineer

AI-Native Software Engineer

Senior AI Learning and Adoption Lead

Senior AI Engineer

Enterprise AI Adoption Lead

Senior AI Engineer – Growth

AI Infrastructure Co-Founder, CSO

Software Engineer, AI Enablement

AI Native Engineer

Mobile Application Developer – AI Finance Agent

AI Delivery Lead