Top Machine Learning Jobs in Manchester

Optimize large-scale ML inference systems for throughput, latency, cold-start time, and cost per token. Tune SGLang, vLLM, and TensorRT-LLM across GPUs and distributed clusters; improve batching, scheduling, KV caching, quantization, weight loading, JIT caching, and multi-node inference. Profile and instrument systems to validate performance gains, eliminate storage and networking bottlenecks, and establish technical direction through reproducible benchmarks and experiments.

Leads Tide’s global engineering organization for ongoing monitoring, fraud detection, financial crime, sanctions, and regulatory-risk platforms. Owns engineering strategy, shared decisioning capabilities, ML model lifecycle, agentic AI governance, platform scalability, and measurable risk and efficiency outcomes. The role manages distributed engineering, data science, and analytics teams while aligning risk, compliance, operations, payments, product, and technology stakeholders across multiple markets.

Build and operate scalable cloud data pipelines, warehouses, transformation layers, and ML production systems. Lead data infrastructure architecture, warehouse performance optimization, DataOps monitoring, CI/CD improvements, data quality testing, and large-scale data product delivery. Collaborate across teams, promote data stewardship, and support batch and streaming pipelines while deploying, serving, and monitoring machine learning models.

Leads and develops a team of Data Scientists and other Data professionals, setting strategic direction and identifying high-impact opportunities. Partners with Product, Engineering, and senior stakeholders to shape product strategy and influence decisions through experimentation, causal inference, statistical modelling, machine learning, and analytics. Ensures analytical work drives measurable customer and commercial outcomes while collaborating with other data disciplines on tooling and infrastructure. Also contributes to hiring, coaching, technical standards, and the broader Data Science practice.

Lead a financial crime data science team focused on detecting, preventing, and mitigating financial crime. Partner with Product, Risk, Engineering, and control owners to shape data strategy, generate strategic insights, improve product decisions, and deliver rigorous analytics and machine learning solutions. Hire, develop, and retain data scientists and analysts while supporting innovation through business intelligence, experimentation, causal inference, and self-serve analytics.

Conduct independent research on frontier AI evaluation and auditing. Design rigorous evaluations, audit methods, and reproducible experimental pipelines; analyze model capabilities, risks, robustness, and alignment-relevant behavior. Produce audit reports, recommendations, publications, tools, and presentations while collaborating with technical, policy, engineering, and standards teams. The role requires careful handling of confidential information and may involve pilot audits with frontier AI companies.

Build Paddle’s data science function and production decisioning systems from the ground up. Prioritize high-value use cases, personally deliver the first ML or agentic system, and own deployment, monitoring, experimentation, reliability, governance, and financial impact. The role will establish the production ML stack, collaborate across Product, Engineering, Risk, Finance, and Compliance, then hire and lead data scientists and ML engineers. Experience with regulated transactional domains, production ML operations, experimentation, and executive stakeholder management is required.

Lead end-to-end delivery of complex Industrial AI and digital transformation projects, from kickoff through deployment, adoption, and value realization. Own scope, schedules, budgets, quality, financial performance, risks, governance, and customer outcomes. Partner with technical teams and executive stakeholders, manage steering committees, facilitate value realization workshops, support change management and training, and identify expansion opportunities. Apply PMO methodologies and continuous improvement practices across enterprise software and industrial technology implementations.

Research, design, and develop algorithms and software systems for AI-driven CNC machining, including toolpath optimization, cutting strategies, and automated CAM workflows. Collaborate with manufacturing experts, data scientists, and software engineers to convert advanced concepts into reliable production software. The role emphasizes high-performance C++ development, computational geometry, numerical methods, optimization, and modern software engineering practices.

Leads strategic AI and machine learning initiatives from concept through production. Designs scalable solutions connecting data, models, platforms, and systems; develops reusable engineering patterns; improves solution quality and reliability; conducts analysis supporting strategic decisions; builds stakeholder partnerships; and provides organizational leadership, mentoring, and innovation across the enterprise.

Designs, develops, and deploys production-grade machine learning systems focused on LLMs, Generative AI, and deep learning. Responsibilities include building scalable ML libraries and research workflows, optimizing distributed training and inference, improving experiment tracking and model lifecycle management, and bridging research prototypes with robust implementations. The role requires collaboration with data science and platform teams, strong software engineering practices, cloud and MLOps expertise, and mentoring across the technical team. Biomedical, clinical, or omics data experience is preferred.

Organize and maintain sales playbooks, battlecards, objection-handling guides, and competitive content. Design onboarding and ramp programs, deliver ongoing sales coaching, identify training gaps, and coordinate content development with instructional designers. Manage enablement content and adoption within Docebo, using usage data to improve effectiveness. Partner cross-functionally with Sales, Marketing, and Product in a B2B SaaS environment.

Lead and mentor a data science team building ML and LLM solutions for cybersecurity. Translate business needs into research and production ML objectives, prioritize roadmaps, collaborate cross-functionally, and drive technical vision, model development, and operationalization for malware detection and threat analysis.

Develop and optimize machine learning and generative AI solutions for digital agents, including LLMs, NLP, RAG, retrieval, experimentation, evaluation, and statistical analysis. Build scalable data and ML pipelines using Python, SQL, and Spark; assess model quality, reliability, safety, and business impact; and support production deployment. Collaborate with engineering, product, and data science teams while mentoring less experienced colleagues.

Guide clients through Cordial platform implementations, third-party integrations, API and JavaScript integrations, data modeling, dynamic scripting, and machine learning capabilities. Serve as a technical subject matter expert, resolve complex issues, manage projects and stakeholder expectations, document solutions, and collaborate with Product, Engineering, Client Success, Support, Strategy, Sales, and partners to drive adoption and successful client outcomes.

Designs and develops scalable AI-powered solutions for enterprise data and analytics problems. Translates ambiguous business needs into algorithmic approaches, builds model pipelines and decision systems, integrates AI capabilities into enterprise platforms, evaluates emerging methodologies, and documents solution architectures. Requires strong software development, machine learning, statistical, mathematical, system design, communication, and large-dataset skills.

Lead pricing ML for Rosso: define technical direction, build production-grade price-optimisation and probabilistic imbalance models, bridge modelling and MLOps, and communicate model decisions across teams.

Develop and validate agentic AI capabilities for patent analysis using machine learning, NLP, LLMs, RAG, hybrid search, and agentic workflows. Analyze large-scale intellectual property datasets, design experiments, evaluate and benchmark models, define reasoning strategies, and translate findings into product recommendations. Collaborate with product managers, IP experts, and engineers to deliver scalable, customer-facing solutions.

Develop and validate agentic AI capabilities for patent analysis using machine learning, NLP, LLMs, generative AI, retrieval, and hybrid search. Define multi-step reasoning workflows, analyze large-scale intellectual property datasets, establish model evaluation and benchmarking practices, and translate experiments into product recommendations. Collaborate with product teams, intellectual property experts, and engineers to deliver scalable, customer-facing solutions.

Lead the execution of production ML systems: build data pipelines, training workflows, evaluation and inference infrastructure, fine-tune large models (LoRA/QLoRA/SFT/DPO), optimize GPU deployments, and integrate ML into products while ensuring robustness, safety, and reliable user-facing performance.

Lead implementation of production-grade ML systems: data pipelines, training/evaluation workflows, scalable GPU inference, model fine-tuning (LoRA/QLoRA/SFT/DPO), deployment, and monitoring. Collaborate with product and application teams to ship reliable, efficient, and safe ML-powered features for real users while managing latency, cost, and robustness constraints.

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.

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.

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

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.