Top Machine Learning Jobs in Manchester

The Senior AI Transformation Consultant works with clients to identify AI opportunities, define robust business cases, and oversee AI project deployments, ensuring solutions deliver value.

One-year, full-time fellowship supporting design, delivery, and evaluation of interventions for smallholder farmers. Duties include research and analysis, pilot design, field implementation, M&E, and cross-team collaboration with heavy mentorship and rotating in-country placements.

Lead delivery of AI-powered insights for clients, manage relationships, and drive commercial growth while innovating technical workflows.

Deliver and migrate production ML data pipelines from Databricks to an AWS SageMaker MLOps platform. Build feature engineering pipelines and feature store integrations, ensure parity/testing with Databricks outputs, manage storage/permissions/IAM, deploy via CI/CD and IaC, document runbooks, and collaborate with engineering, AWS ProServe, and client teams in a regulated iGaming environment.

Lead ML strategy and delivery for Workforce Management: forecasting, routing, assignment, escalation and time-to-completion prediction. Build, ship and maintain production ML systems, coach and grow a team of ML scientists, work cross-functionally to turn operational problems into pragmatic ML solutions, and improve customer and operational outcomes.

Lead the development and optimization of machine learning algorithms, owning the end-to-end model development and collaborating with Data Engineers for integration into live systems.

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

The role involves designing and developing AI-powered solutions for complex enterprise problems, integrating AI capabilities, and collaborating with various teams. Candidates need strong analytical, algorithmic, and engineering skills in AI and ML.

Develop and evaluate AI/ML and Generative AI/LLM models from POC to production, perform data science and statistical analyses to support business decisions, document and assess model risk, publish patents/papers, collaborate with SMEs, and provide technical leadership while deploying solutions with modern Agile/DevOps and cloud practices.

Develop, deploy, and maintain scalable AI/ML pipelines and production models, including LLMs and generative AI. Optimize model performance, latency, and cost across AWS, GCP, and Azure. Apply MLOps practices such as CI/CD, versioning, monitoring, logging, and maintenance. Collaborate with data scientists, software engineers, solution architects, and cross-functional teams to integrate AI capabilities into products and workflows.

Lead development of enterprise commercial intelligence: build data models (pipeline, forecasting, segmentation), deploy AI/ML for forecasting and churn, create dashboards, establish data governance, partner with senior leaders to drive insight-led decisions and adoption across global teams.

The Research Manager will lead AI-powered insight projects, manage client relationships, oversee team execution, and innovate using AI tools to improve insights delivery.

Lead statistical planning and delivery for clinical studies using R and RShiny, mentor biostatistics staff, prepare SAPs and programming specifications, perform quality control, support submissions and regulatory interactions, and develop/maintain R-based visualization apps and infrastructure.

Lead enterprise sales for Everpure's AI/HPC data infrastructure, driving large deals with F1000 companies. Build executive relationships, lead cross-functional pursuit teams, influence product roadmap, manage a pipeline to exceed revenue targets, and evangelize all-flash storage and data pipeline solutions for AI workloads.

This role involves leading the development of AI systems to replace lab toxicity experiments, focusing on end-to-end ML architecture, research on chemistry-biology correlations, and building innovative models. Candidates should have strong ML skills, engineering abilities in Pytorch and Python, and a passion for both science and technology.

Build and deploy AI-powered applications, agents, copilots, and intelligent workflows for enterprise clients. Translate ambiguous business problems into scalable architectures and production software using LLMs, Generative AI, APIs, enterprise systems, and data sources. Rapidly prototype solutions, validate them with users, and implement monitoring, evaluation, and feedback mechanisms. Collaborate with technical and business stakeholders to move successful AI solutions into production while clearly communicating recommendations.

The role involves analyzing biological mass spectrometry data, developing software tools, and scaling workflows for AI and human consumption in drug safety assessment.

Lead end-to-end machine learning systems across data, training, fine-tuning, evaluation, inference, deployment, and monitoring. Build scalable GPU-based infrastructure and reliable production pipelines for large models, optimize performance and cost, and partner with research and application engineering to translate model capabilities into product improvements. Provide hands-on technical leadership, resolve production issues, and ensure measurable, safe, and maintainable ML iterations.

Industry Expert who sets the technical bar for advanced AI, ML, systems, or forward-deployed engineering programs. Responsibilities include reviewing deployed projects, leading technical defenses and mock review boards, delivering occasional live sessions, critiquing architecture and tradeoffs, and advising instructors on difficult design decisions. The role requires production-scale engineering experience, senior-level judgment, a public technical footprint, strong English, and comfort with AI tools. It is fully remote and requires approximately 4–10 hours monthly.

Manage requests for new ServiceNow reports and dashboards, resolve dashboard/reporting incidents, train users on dashboards, build executive KPIs and scorecards, engage stakeholders to gather requirements, and apply AI/Generative AI for reporting automation and insights.

As a Lead Data Scientist, you will set technical direction, ensure project delivery, manage client relationships, and mentor data scientists.

The Computer Vision Engineer will lead AI initiatives in the defense sector, delivering solutions and mentoring teams while driving technical strategy and capability development.

Lead commercial expansion into the UK Air domain, owning sales, pipeline, bids, contracts and account growth. Build senior customer and partner relationships, translate AI/ML capability into operational outcomes, and ensure solutions are deliverable and strategically aligned while working with multidisciplinary teams in secure defence contexts.

Own commercial strategy and operations for enterprise pharma and AI accounts: drive pilots-to-revenue, account expansion, pipeline management, pricing, forecasting, and deal strategy. Build cross-functional relationships, push deals through legal/procurement/IT, act as CEO counterpart to shape strategy and product roadmap, and be highly present in person with customers and at conferences.

Lead end-to-end research applying advanced ML/DL (transformers, GNNs, RL, ensembles) to generate alpha across digital asset markets, build production-grade pipelines, prevent leakage/overfitting, analyze microstructure and on-chain data, and mentor junior researchers.