Top Machine Learning Jobs in Manchester

Build and operate the internal developer platform used by AI researchers and ML engineers. Responsibilities include secure development environments, container image builds, CI/CD tooling, training instrumentation, self-service debugging tools, automation, infrastructure as code, observability, and production troubleshooting. The role also includes establishing infrastructure best practices, collaborating with research teams, and participating in a compensated 24/7 on-call rotation.

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.

Build and own pricing data science capabilities, including demand and elasticity models, forecasting, willingness-to-pay models, pricing optimization, experimentation, and causal analysis. Deploy and monitor machine learning systems, establish governed pricing metrics, and partner with Commercial, Finance, and Product to improve margin and revenue. Set analytical standards for pricing decisions and build reusable analytical capabilities.

Leads product strategy execution for a domain, manages and develops Product Managers, drives portfolio prioritization and OKRs, and ensures measurable outcomes. Coaches teams on discovery, AI-first workflows, customer engagement, stakeholder alignment, and regulated GxP software development. Partners with strategy, engineering, sales, and customer-facing teams while maintaining product quality, regulatory readiness, and cohesive cross-domain execution.

Leads the identification, development, integration, and global deployment of AI and automation solutions for legal editorial, metadata, and content operations. The role supports AI strategy, evaluates designs against governance and quality standards, manages cross-functional projects, reports data-driven outcomes, tracks industry trends, documents improvements, and promotes responsible AI use.

Own the B2B sales pipeline and revenue for AI and data services. Responsibilities include outbound prospecting, qualifying technical buyers, managing complex pilots and recurring contracts, developing partnerships and channels, shaping pricing and commercial terms, protecting margins, and feeding customer demand into product and operations. The role requires experience selling AI/ML data, annotation, licensing, or related technical services to research and enterprise buyers.

Lead hands-on development of production-grade AI systems, including RAG pipelines, semantic search, embedding infrastructure, LLM orchestration, and integrations with legacy and multi-cloud environments. Establish engineering standards, mentor developers, implement responsible AI guardrails, and ensure secure, observable, reliable deployments. Support knowledge transfer to government engineers, promote sustainable AI practices, and work with multidisciplinary public-sector teams. The role is hybrid in Newcastle and requires weekly travel there, UK-only system and data access, and BPSS clearance.

Lead NVIDIA’s engagement with AI-native startups, scaleups, and labs. Understand customer needs, develop AI technology solutions, coordinate technical and business resources, drive adoption and partner growth, create customer references and case studies, identify emerging use cases, and represent NVIDIA at industry events.

Owns strategy and growth for a defined market segment within CluePoints’ clinical-trial SaaS portfolio. Responsibilities include developing go-to-market strategies, coordinating product, marketing, sales, consulting, customer success, and finance teams, enabling teams to launch and support products, advising strategic customers, and leading user communities. Success is measured through market penetration, win rate, retention, NPS, and gross margin.

Own and build the company’s metadata-driven data layer as a hands-on principal architect. Design schema-flexible and versioned platforms with lineage, auditability, semantic mapping, and resilience to schema changes. Write production code, establish AI-assisted engineering practices with verification, coach engineers, and communicate architecture to technical and business stakeholders. Experience with regulated data, multi-tenant SaaS, semantic technologies, or clinical data standards is valuable.

Serve as a customer-facing fraud and identity expert across pre-sales, onboarding, and expansion. Design risk strategies and identity workflows, advise customers on fraud mitigation, coordinate technical and regulatory implementation requirements, validate milestones, and lead go-live readiness. Partner cross-functionally with Product, Engineering, Legal, and Compliance teams to accelerate deployment, adoption, and customer value.

Leads the demand generation engine, translating go-to-market priorities into segmented campaigns across email, search, social, content, webinars, and events. Owns marketing automation, lead scoring, Salesforce integration, pipeline reporting, conversion metrics, budgets, and agency performance. Partners with Sales and Commercial Operations to improve handoffs and qualified opportunities, applies AI and automation to campaign execution, and recruits and coaches future demand generation team members.

Build and deploy end-to-end data science and AI solutions, including scalable data pipelines, machine learning and generative AI workflows. Translate product and business questions into measurable analytics use cases, analyze product data, establish evaluation and monitoring practices, and collaborate with product, engineering, and analytics teams to deliver business impact.

Lead the transformation of legacy products into a unified enterprise platform. Translate strategy into roadmaps, manage backlogs, prioritize cross-functional initiatives, and coordinate Engineering, UX, AI, Data Science, Commercial, and Operations teams. Drive platform discovery, delivery, launch, adoption, and measurable business outcomes. Integrate trustworthy AI and data capabilities into customer-facing workflows while influencing senior stakeholders and balancing customer value, commercial goals, technical feasibility, risk, and resources.

Leads and grows a machine learning engineering team responsible for production ML infrastructure, automated training, model evaluation, MLOps pipelines, observability, and low-latency model serving. Provides technical architecture guidance, partners with product and data teams on roadmaps, promotes engineering best practices, and supports technical hiring. The role focuses on reliable ML systems processing large, real-time datasets, with potential emphasis on streaming, time-series, and spatial-temporal applications.

Lead the direction of Data Science across Autotrader by identifying opportunities, shaping and orchestrating ML-focused projects, and driving commercial value. Collaborate with cross-functional leadership, analysts, and software engineers; establish analytical standards; mentor data professionals; and develop data products using modern ML frameworks, workflows, and cloud technologies.

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.

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.

Apply AI and machine learning to large, disparate datasets to detect fraud and cybersecurity threats. Build analytic solutions in Databricks using Python, SQL, and R; automate reusable pipelines; investigate stakeholder concerns; identify process gaps; recommend remediation; and communicate findings to technical and executive audiences. The role also involves directing specialized analytics teams and applying large language models to accelerate analysis and development.

Evaluate and deploy AI models on mobile devices, benchmark performance, integrate models into Android applications and services, and optimize latency, memory, power, and hardware acceleration. The role also develops hybrid local/cloud inference routing and collaborates with AI agent engineers to integrate on-device models into agent runtimes.

Lead the architecture and development of ENSEK’s shared AI/ML platform. Establish scalable MLOps and LLMOps capabilities covering model deployment, evaluation, observability, governance, RAG, agentic workflows, safety, and lifecycle management. Partner with data and platform teams to operationalize AI solutions, mentor engineers, define technical standards, and build a high-performing AI engineering capability supporting multiple product teams in a regulated environment.

As a Research Engineer, you'll develop and manage data systems for text-to-speech projects and enhance machine learning models using innovative strategies.

Leads the design, development, deployment, and evaluation of machine learning, forecasting, recommendation, and analytical solutions. Partners with Product, Commercial, Engineering, and Data teams to solve business problems, define metrics, build scalable pipelines, and deliver measurable impact. Provides technical leadership and mentorship, promotes model development and governance best practices, and contributes to the data science roadmap.

Lead GitLab’s company-wide strategy for autonomous, agentic software development workflows. Identify and validate AI opportunities across the SDLC, build prototypes and reference architectures, establish evaluation frameworks and safety controls, and drive internal adoption. Partner across Engineering, Product, Infrastructure, Security, Architecture, and Data and ML teams to scale reliable, observable, compliant agentic capabilities into customer-facing products. Mentor senior technical leaders and represent GitLab externally on AI-assisted development and productivity measurement.