Top Machine Learning Jobs in Belfast

Drive product and business impact through statistical modeling, experiment design, Bayesian marketing analysis, machine learning, NLP, LLM applications, and classifier implementation. Build growth metrics, monitoring pipelines, evaluation datasets, and AI trigger systems across search and Duck.ai. Analyze privacy-constrained data, deploy production-grade models, optimize SQL queries, and lead complex projects from scoping through production with minimal direction.

Software engineering intern supporting the Platform Engines group and Policy Engine for SailPoint’s cloud identity security platform. Responsibilities include developing software, procedures, and test solutions; collaborating with software and QA engineers, technical writers, and product managers; and working in Agile teams. The intern will gain exposure to cloud and software technologies including Golang, Angular, AWS, Docker, Kafka, Kubernetes, REST, SQL, and Temporal.

A 12-month graduate technology programme involving three rotations across software engineering, data, AI, banking, and compliance technology teams. Responsibilities include designing, building, testing, debugging, deploying, and improving software, platforms, applications, APIs, and data capabilities. Graduates will collaborate globally, gain exposure to analytics, automation, machine learning, and generative AI, and deliver practical technology solutions within a financial-services environment.

Develop and support credit trading risk, P&L, flow analytics, reporting, and pre-trade systems. Partner with traders and sales to gather requirements, design software architecture, analyze curated datasets, and build production-quality applications. Create relative-value metrics, back-testing tools, portfolio optimization models, flow matching, and trade prospect rankings. Apply quantitative modeling, machine learning, NLP, and AI to improve trading and sales decision-making.

Design and deliver enterprise-grade machine learning systems and AI-powered applications for banking. Build scalable, secure, distributed applications and automated cloud, desktop, and ML pipelines. Apply MLOps practices for versioning, reproducibility, and observability while collaborating with cloud and site reliability engineering teams. Develop reusable libraries and business-critical, data-intensive solutions, aligning machine learning problems with business objectives. The role may include mentoring or people leadership.

Thirteen-month technical internship in SAP’s Belfast office, offering placements in data DevOps, cloud infrastructure and validation, or hyperscaler cloud engineering. Responsibilities include developing containerized infrastructure, Kubernetes environments, CI/CD pipelines, Django-based testing services, performance automation, and deployment tools across AWS, Azure, GCP, and SAP Cloud Infrastructure. Candidates will collaborate with engineering teams and gain exposure to AI and machine learning workloads.

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 support secure full-stack web applications, internal tools, APIs, data pipelines, and database schemas. Develop AI agents and LLM-powered solutions using Agentic AI, RAG, tool calling, guardrails, and Microsoft Foundry. Deploy machine learning models for predictive analytics, implement automated testing and CI/CD, resolve application and data issues, and modernize legacy systems. Collaborate with technical teams and business stakeholders to deliver scalable information and decision-support solutions.

Leads economists, econometricians, and data analysts delivering applied economic, econometric, machine learning, and policy analysis for communications regulation. Responsibilities include directing concurrent projects, evaluating policies, shaping regulatory decisions, communicating findings to stakeholders, producing publications, and mentoring colleagues. The role requires extensive real-world economic analysis experience, strong applied microeconomics and econometrics expertise, analytical software proficiency, and the ability to lead teams and develop innovative quantitative approaches.

Provides statistical leadership for Medical Affairs, real-world evidence, comparative effectiveness, HTA, and HEOR activities. Designs and analyzes observational, clinical, Phase IV, and indirect treatment comparison studies; evaluates data quality, bias, uncertainty, and applicability; supports evidence-generation strategies, reimbursement submissions, publications, and scientific communications. Collaborates with statistics, epidemiology, RWE, medical, and external scientific teams to translate complex analyses into actionable conclusions.

The Principal Solutions Architect leads technical solution design, partners with hyperscalers, ensures architectural integrity, and drives customer engagement through compelling narratives and demos.

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.

Own and improve production machine learning pipelines focused on accuracy, efficiency, and trustworthiness. Build LLM evaluations, judges, datasets, metrics, and benchmarks; run experiments across prompts, models, and pipeline designs; and reduce LLM costs through smaller task-specific models. Expand anonymization capabilities, evaluate open-source models, and collaborate with quality engineers to ship reliable ML features. Document findings and communicate technical trade-offs clearly.

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.

Supports enterprise sales by discovering customer requirements, mapping them to SailPoint solutions, and delivering foundational implementations and proof-of-value engagements. Acts as a technical lead and consultative advisor, educating customers on architecture and best practices. Collaborates with sales, product, professional services, partners, and customer success teams while developing AI-driven and agentic solutions for demonstrations and customer outcomes.

Build and improve Firefox WebCompat internal dashboards, reporting tools, and broken-site screenshot capture pipelines. The role involves creating reliable data views for triage and metrics, automating screenshot workflows, collaborating with WebCompat and QA teams, and translating reporting needs into effective tooling. Familiarity with WebCompat workflows, Bugzilla, screenshot automation, and machine learning is preferred.

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 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.

Entry-level placement role developing broad technology skills through an AI-first academy and early careers programme. Responsibilities include contributing to software solutions, testing, cloud and platform engineering, data and AI, low-code development, cybersecurity, and solution delivery. The role involves collaborating with technical and non-technical teams, using AI-assisted tools responsibly, learning across delivery disciplines, and progressively taking ownership of more complex work.

Graduate technologists complete an AI-first academy and early careers program, gaining practical experience across software engineering, QA, data and AI, low-code development, cloud platforms, DevOps, and cybersecurity. They contribute to technology solution design, implementation, testing, deployment, and support while collaborating with technical and non-technical teams. The role emphasizes continuous learning, responsible use of AI-assisted tools, adaptability, communication, and development toward future technical leadership.

Manage NVIDIA’s relationships with AI-native startups, scaleups, and labs. Identify customer needs, develop AI solution strategies, drive adoption of NVIDIA platforms, coordinate cross-functional and partner resources, create customer references and case studies, develop emerging use cases, and represent NVIDIA at industry events. The role requires strategic market analysis, AI business development expertise, ecosystem relationships, and strong communication skills.

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.

Own the strategy, roadmap, development lifecycle, and go-to-market activities for fraud analytics products. Lead customer discovery, prioritize product improvements, analyze complex data, collaborate with development and data science teams, and use market and customer insights to improve user outcomes. The role requires fraud prevention expertise, statistical modeling and AI/machine learning knowledge, strong stakeholder communication, and flexible UK-US working hours.