Top Machine Learning Jobs in Manchester

Lead technically complex product work on Rosso, embedding with ML, data science and engineering teams to turn business and customer needs into shipped features. Define success criteria for models/forecasts, manage edge cases and operational risk, and prioritize work across portfolio, forecasting, pricing optimisation and related systems.

Leads the design and scaling of Databricks-based data, analytics, and AI platforms supporting global customer operations. Builds enterprise data pipelines, machine learning, NLP, generative AI, conversational AI, dashboards, and operational intelligence solutions. Establishes governance, data quality, CI/CD, reliability, and AI lifecycle standards while partnering with product, operations, engineering, workforce management, and AI teams. Provides technical leadership, coaching, and mentorship to engineers and data scientists.

Participate in a 10-week data science engineering internship by applying analytical methods to business and engineering problems. Analyze and profile data, perform feature engineering, identify patterns and anomalies, and build automated models and analytical tools. Collaborate with engineering and business teams to translate insights into recommendations and scalable data-driven solutions. Gain experience in programming, advanced analytics, machine learning, distributed computing, and communicating complex results.

Lead Mitek’s EMEA revenue strategy and execution across direct enterprise and channel sales for identity and fraud prevention platforms. Own regional growth, forecasting, pipeline, account expansion, partner ecosystem development, customer adoption, and revenue targets. Manage enterprise sales and account teams, negotiate complex multi-year agreements, support regulated-market customers, and report performance to the CRO while collaborating with Product, Engineering, Finance, Legal, Compliance, Customer Success, and global channel teams.

Serve as the technical SME for compliance automation, overseeing GRC control mapping, evidence collection, integrations, and continuous monitoring. Partner with engineering teams to embed compliance into workflows, support audits and framework rollouts, assign control ownership, reduce audit burden, and maintain audit readiness. Lead cross-functional initiatives and communicate compliance risks, control status, and progress to technical and leadership audiences. The role requires cybersecurity framework expertise, program management, cloud and API fluency, GRC platform experience, and familiarity with AI-enabled compliance workflows.

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

Design and develop AI solutions for complex problems, integrating AI capabilities into systems and improving effectiveness through experimentation and evaluation.

Reviews and evaluates engineering, infrastructure, and platform documents and work products for technical accuracy, completeness, clarity, consistency, and relevance. Provides structured feedback, identifies errors and gaps, compares technical approaches, and follows defined evaluation frameworks. Requires independent work, strong communication, analytical ability, and professional engineering judgment. The role is fully remote and supports varied technical evaluation projects as an independent contractor.

Build and deploy machine learning and NLP models, develop end-to-end modeling pipelines, use BigQuery ML for data processing and training, prototype generative AI solutions, and mentor junior team members. The role requires hands-on experience with ML engineering, NLP, LLM concepts, embeddings, prompting, and exposure to agentic systems.

Own an AI stockbroking product vertical end-to-end, including strategy, metrics, roadmap, technical design, delivery, and launch. Partner with engineering on APIs, data flows, ledgering, security, scalability, and failure handling. Integrate KYC, transaction monitoring, privacy, licensing, and regulatory requirements. Apply AI with evaluation, safeguards, and human oversight while coordinating Engineering, Design, Compliance, Operations, Treasury, and Finance.

Own an AI investing product vertical end-to-end, including strategy, roadmap, technical design, delivery, metrics, and launch. Partner with engineering on APIs, data flows, ledgering, reconciliation, security, scalability, and failure handling. Embed regulatory requirements across KYC, monitoring, limits, privacy, and licensing. Apply AI with evaluation, safeguards, human oversight, and reliability controls while aligning compliance, operations, finance, treasury, and design.

Owns an AI neobank product vertical end-to-end, including strategy, roadmap, technical design, delivery, regulatory requirements, AI evaluation, reliability, and launch. Partners with engineering, compliance, operations, treasury, finance, and design across payments, remittance, insurance, wealth, and trading products. Must balance correctness, scalability, security, user experience, cost, speed, and regulatory constraints in high-consequence financial systems.

Develop and test Generative AI and LLM applications in Python, including RAG systems, prompt workflows, AI agents, and API integrations. Evaluate model performance, reliability, accuracy, and responsible AI considerations while collaborating with engineering, data science, and product teams. Contribute to documentation and Git-based development, learn emerging AI technologies, and help deliver production-ready solutions.

Owns roadmap execution and capability definition for an Agentic AI Platform. Translates AI platform strategy into requirements, prioritized roadmaps, reusable agent orchestration and lifecycle capabilities, governance controls, observability, and scalable platform patterns. Partners with engineering, architecture, data, security, legal, compliance, customer success, professional services, and customers. Measures adoption, reliability, safety, quality, cost, and business impact while balancing technical feasibility, security, compliance, and customer value.

Lead statistical strategy and delivery for Phase I-IV clinical trials embedded with a pharmaceutical sponsor. Design analyses, support protocol and reporting activities, oversee programming/CRO work, apply SAS/R (R Shiny)/Python and ML/DL methods for analyses and automation, and mentor junior statisticians.

Lead technical pre-sales for AI and data opportunities across the UK public sector. Shape solution strategies, conduct client workshops, lead bids and procurements, advise on responsible AI adoption, and develop differentiated AI propositions. Provide expertise in generative and agentic AI, machine learning, LLM solutions, data foundations, governance, evaluation, and production adoption. Build senior stakeholder relationships, support demonstrations and proofs of concept, mentor colleagues, and ensure proposed solutions are practical, secure, scalable, ethical, and deliverable.

Build and extend the platform layer: design shared backend services and APIs, improve reliability and correctness, collaborate with product teams to remove platform bottlenecks, mentor engineers, shape architecture and migrations, and write high-quality fullstack code with a backend focus.

Own the end-to-end product direction and quality of AI-powered email capabilities. Define requirements, evaluation frameworks, quality standards, and measurable outcomes while balancing model quality, latency, cost, reliability, safety, and user experience. Partner closely with ML, backend, and client engineers on system design, experimentation, failure handling, and iteration. Drive prioritization, technical decisions, execution, and user trust in a fast-moving zero-to-one environment.

Own the end-to-end product strategy and execution for an AI-powered email application. Define AI behavior, system requirements, evaluation frameworks, quality standards, and user experiences while balancing reliability, latency, cost, safety, and usability. Partner closely with ML, backend, and client engineering teams to identify failure modes, improve system performance, prioritize work, and deliver predictable, trustworthy AI capabilities in a fast-moving zero-to-one environment.

Own the end-to-end product direction and quality of an AI-powered email application. Define AI behavior, system requirements, evaluation frameworks, quality standards, and user experiences while balancing quality, latency, cost, reliability, safety, and usability. Partner closely with ML, backend, and client engineers to identify failure modes, prioritize improvements, run experiments, and deliver predictable, trustworthy AI features in a fast-moving zero-to-one environment.

Owns the end-to-end definition, evaluation, and quality of AI-powered email capabilities. Partners with ML, backend, client, and product teams to translate model behavior and technical constraints into requirements, system decisions, experiments, and roadmaps. Balances quality, latency, cost, safety, reliability, and user experience while identifying failure modes and building trustworthy, predictable AI features.

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.