Top Machine Learning Jobs in Manchester

Lead end-to-end ML initiatives: integrate and fine-tune models, optimize GPU inference for latency and throughput, build evaluation and monitoring tooling, collaborate on scalable serving systems, and mentor engineers.

Serve as the technical AI/ML lead in customer engagements: translate customer needs into robust solution designs, build stakeholder relationships, contribute to proposals and product roadmaps, prototype approaches, and ensure delivered ML systems are operationally valuable.

Lead and scale Mimica's product organisation, define global product strategy and roadmap for AI-driven task/process mining and automation, mentor product and design teams, improve product operations and discovery, align cross-functional stakeholders, handle escalations, and hire and forecast team needs.

As a Senior Impact Analyst, you will analyze sustainability challenges, design and manage interventions, and work closely with smallholder communities, emphasizing rigorous research and collaboration.

Embed with engineering clients to own full-cycle technical hiring for software, AI/ML, infrastructure and data roles. Run deep intakes, headhunt passive candidates, coach hiring managers on interview design and calibration, build hiring foundations, report on pipeline health, and use/builder recruitment AI agents to scale impact.

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.

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.

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.

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 and grow a team of ML scientists to build and productionise credit underwriting, pricing, limits, and lifecycle models. Define Borrowing's ML roadmap for UK growth, new segments, and EU expansion, improve feature engineering and data use, and raise standards for model quality, governance, and iteration speed.

Lead enterprise AI strategy and roadmap; build and scale cross-functional AI/ML teams; architect and deliver LLM, RAG, predictive and data-heavy AI products; establish MLOps, governance, cloud-native platforms (Azure/Databricks/AWS/GCP); drive product lifecycle, KPI tracking, security, and responsible AI practices.

Identify, qualify, and nurture enterprise leads for Cohere's AI solutions through outbound outreach, BANT qualification, Salesforce pipeline management, and collaboration with marketing. Run smaller sales cycles to close opportunities and stay current on industry and product trends.

As a P&C Actuary, you will assess risks for insurers, support audit teams, and maintain compliance with regulations, while growing client relationships and improving tools for efficiency.

Own and grow the EMEA territory by building pipeline, managing full sales cycles, closing enterprise travel-tech deals, and developing partnerships with TMCs and travel providers. Represent the company at events, engage senior executives, and share market feedback with Product and Leadership to drive strategic outcomes.

As a Python and Kubernetes Software Engineer, you will develop data analytics solutions using open source tools, collaborating with a distributed team.

The Senior ML Data Engineer will enhance data workflows, collaborate with teams to build datasets, influence data strategies, and implement pipelines for effective data usage in AI/ML products.

Full-stack AI/ML Software Engineer to build HealthTech SaaS microservices, implement and integrate ML solutions, write high-quality code, author documentation, perform code reviews, and act as an SME on services and systems.

Build and validate production-grade ML and AI systems across pricing, underwriting, claims, and portfolio management. Work with structured and unstructured data, LLMs and agentic workflows, extract information from documents, resolve entities, create feature pipelines and inference services, quantify uncertainty, monitor drift, and measure business impact in collaboration with engineers, actuaries, underwriters, and product teams.

Lead and own ML platform end-to-end: data/feature pipelines, training and inference infrastructure, model registry, deployment, monitoring, and agentic AI stack. Design integrations across teams and third parties, ensure reproducibility, reliability, cost control, governance and audit trails, and grow a small engineering function supporting data scientists.

Lead architecture and technical strategy for Anaplan's predictive intelligence services. Design and operate ML platforms, model-serving, and data pipelines across multi-cloud Kubernetes environments. Define MLOps strategy, establish best practices for scalability and observability, mentor senior engineers, partner with data science and product teams, and drive cross-organization roadmap and architectural alignment.

As a Staff/Principal Machine Learning Engineer, you will optimize and scale multimodal inference systems, ensuring high performance and reliability in production environments.

Join Starling as a Software Engineer to design, implement, and maintain ML-powered features, collaborating with engineers and data scientists, while delivering impactful solutions in a hybrid working environment.

As a Machine Learning Engineer, you will develop production-ready ML solutions, collaborate with multidisciplinary teams, and contribute to Defence and National Security challenges.

Build and operate ML infrastructure for training, evaluation, deployment, inference, and experimentation. Improve reliability, scalability, latency, throughput, and cost efficiency. Create reproducible pipelines, observability, benchmarking, and platform tooling enabling researchers and engineers to ship models to production.