Top Machine Learning Jobs in Belfast

Lead and deliver statistical support for Phase II-III clinical trials embedded with a sponsor: design input, SAP/sample size, interim/final analyses, oversight of programmers/statisticians, regulatory submissions, and collaborate cross-functionally to ensure compliant, high-quality statistical deliverables. Apply advanced statistical methods and ML/DL for analytics and process automation.

Drive data-informed capital allocation by building financial models (NPV, IRR), applying AI/ML for forecasting and scenario planning, creating build-vs-buy and prioritization frameworks, implementing decision-support systems, collaborating with cross-functional stakeholders, and monitoring post-investment performance to refine allocation models.

Design, implement, and optimize AI-powered solutions to support financial compliance and controls. Monitor GAAP/IFRS adherence, automate controls and reporting, support internal/external audits, and manage complex accounting areas like revenue recognition and lease accounting while collaborating with finance, audit, and technology teams.

Design, implement, and monitor AI/ML models to improve underwriting risk assessment, pricing, and portfolio management. Translate underwriting rules into data models, integrate models into workflows, analyze datasets for risk patterns, build decision-support tools, ensure compliance and fairness, and collaborate with underwriting, actuarial, product, and engineering teams.

Contract role combining actuarial science, risk management, and AI to improve portfolio performance. Responsibilities include loss and combined ratio analysis, portfolio risk assessment, catastrophe and exposure modeling, stress testing, and integrating ML to enhance pricing, reserving, and risk selection while collaborating with underwriting, finance, and product teams.

The Data Scientist will analyze data, build and refine machine learning models, document technical designs, and collaborate with multidisciplinary teams to deliver AI solutions.

The role involves building infrastructure for training open source foundation models, with emphasis on autonomy and contributing in a dynamic team.

The role involves designing, building, and deploying ML systems, supporting stakeholders, and operationalizing AI applications in Defence.

Lead engineering systems and teams for an AI chat application, focusing on backend, mobile, and desktop. Oversee strategy, architecture, integration, and high-impact technical decisions for reliable user experiences.

As a P&C Actuary, you will assess insurance risks, review regulatory compliance, support audit teams, and document findings while nurturing client relationships and contributing to practice development efforts.

As a UX Writer at Nebius, you'll improve user experience by creating UI copy, collaborating with teams, and developing guidelines.

Manage the end-to-end product lifecycle for financial crime tools, focusing on compliance, innovation, and backlog management while liaising between engineering and business teams.

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, train, evaluate and productionise custom ML and LLM models for customer feedback: fine-tuning pipelines, retrieval/reranking, summarisation, sentiment, evaluation frameworks, and monitoring. Collaborate with engineering, product and commercial teams, mentor colleagues, run experiments, and translate business needs into practical ML solutions.

Lead finance analytics, own data warehouse strategy and governance, build a reporting Center of Excellence, implement Finance AI automation and data pipelines, optimize GCP costs, create self-service analytics, manage a small cross-functional team, and ensure data governance and audit readiness.

Lead pricing ML for a real-time energy transaction engine: define mathematical formulation, build and ship production-grade models, optimise pricing and portfolio balance, and communicate model behavior to commercial and engineering stakeholders.

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.

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 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.

Nebius seeks a Senior ML Engineer for AI R&D focusing on applied research in reinforcement learning and agent training, requiring deep expertise in machine learning and software engineering.

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

Lead development and productionisation of custom ML and LLM systems for customer feedback: fine-tune and evaluate models, build retrieval/reranking and agentic features, design evaluation frameworks, collaborate with Engineering and Product, mentor team members, and drive model-serving and monitoring work.

Build and operate ML platform systems that enable teams to train, evaluate, deploy and serve models. Work on backend services, Python libraries, model lifecycle tooling, evaluation workflows and low‑latency serving using Go/Python, Kubernetes and cloud infrastructure, prioritizing reliability, observability, safety and developer experience.

Design, scale, and productionize ML and physics-based scientific code for drug and materials discovery. Transition prototypes to robust products, orchestrate distributed GPU training, implement hardware optimizations, and support model lifecycle and integration into large-scale simulation frameworks.

Design and build end-to-end ML systems for fraud detection: develop and deploy models, create production-ready features and real-time data pipelines, integrate with backend services, and ensure monitoring, security, and observability.