Top Machine Learning Jobs in Belfast

Drive partner-led CCaaS growth across EMEA and APAC by recruiting, onboarding, and scaling strategic partners. Lead co-selling motions, commercial opportunities, partner enablement, packaged offerings, forecasting, and performance reviews. Translate Zendesk AI and contact center capabilities into partner use cases and revenue opportunities while collaborating with Sales, Marketing, Product, Engineering, Solutions Consulting, and Customer Success teams.

Develop, debug, test, and optimize machine learning models, training pipelines, inference systems, numerical methods, and supporting infrastructure. Build reproducible Python workflows, diagnose computational issues, validate implementations, use coding agents, and document technical findings. This remote role requires approximately 15 hours per week and rapid task availability.

Evaluate AI model outputs on data science, statistics, machine learning, experimentation, and quantitative problems. Develop expert prompts, datasets, and reference solutions; identify statistical and methodological errors; and provide structured feedback to improve model performance. The role requires Python, machine learning, statistics, data analysis, experimentation, Pandas, and data modeling skills.

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.

Serves as a customer-facing fraud and identity subject matter expert across pre-sales, onboarding, and expansion. Advises enterprise customers on risk profiles, fraud strategies, identity workflows, integrations, regulatory hurdles, and implementation best practices. Leads onboarding and go-live readiness, coordinates cross-functional teams, validates milestones, and helps customers optimize verification workflows, reduce friction, accelerate deployment, and maximize product value.

Lead a small team developing secure, reliable agentic AI capabilities. Responsibilities include setting technical direction, building agent monitoring, evaluation, and guardrail frameworks, performing machine learning tasks, translating research into production features, mentoring engineers, and contributing hands-on code and technical designs. The role focuses on agent collaboration, trust, security, quality measurement, and scalable software delivery.

Leads global AI-first MarTech engineering strategy, architecture, governance, compliance, and delivery. Oversees scalable digital platforms, public-facing properties, GenAI applications, R&D, proof-of-concepts, and automated compliance capabilities. Builds and mentors a global team of engineering and AI professionals, manages vendors, and partners with marketing, business, cloud, security, privacy, infrastructure, and legal stakeholders to deliver secure, high-performance digital experiences.

Leads end-to-end AI-powered insight and market research programmes for major client accounts. Responsibilities include shaping briefs, advising clients, managing scopes, budgets, timelines and resources, supporting pitches, leading consultants, and improving research delivery through AI-enabled tools. The role requires commercial research expertise, strong client leadership, organizational skills, and curiosity about emerging AI technologies.

Own the vision, strategy, roadmap, integration, commercialization, and executive stewardship of Kaltura’s new AI-powered learning and enablement platform. Lead 0-to-1 MVP delivery and post-MVP expansion across AI course generation, conversational tutoring, multimodal learning, simulations, and personalized journeys. Partner with engineering and GTM teams on architecture, pricing, packaging, positioning, enterprise sales, and integration of acquired products.

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.

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.

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.

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.

Serve as a customer-facing fraud and identity expert across pre-sales, onboarding, and expansion. Design risk strategies and identity workflows, advise customers on fraud mitigation, coordinate technical and regulatory implementation requirements, validate milestones, and lead go-live readiness. Partner cross-functionally with Product, Engineering, Legal, and Compliance teams to accelerate deployment, adoption, and customer value.

Leads the demand generation engine, translating go-to-market priorities into segmented campaigns across email, search, social, content, webinars, and events. Owns marketing automation, lead scoring, Salesforce integration, pipeline reporting, conversion metrics, budgets, and agency performance. Partners with Sales and Commercial Operations to improve handoffs and qualified opportunities, applies AI and automation to campaign execution, and recruits and coaches future demand generation team members.

Own the market strategy and growth of Luma’s lab automation and orchestration solutions. Engage pharma, biotech, CDMO, and research customers; define segments, personas, positioning, competitive strategy, pricing inputs, and AI use cases. Enable global sales and marketing teams with messaging and sales tools, align Siemens organizations, support strategic pursuits, and build partnerships with automation vendors and thought leaders. Drive software adoption, pipeline growth, and customer movement toward connected and autonomous laboratories.

Engineering Interns contribute to software features across the full development lifecycle, including design, development, testing, deployment, monitoring, and improvement. They work with APIs, databases, streams, and queues while participating in stand-ups and peer-led teams. The role provides mentoring and hands-on experience with Signifyd’s technology stack. Candidates should be pursuing a bachelor’s degree in computer science or software engineering, show interest in software development and machine learning, and be curious, collaborative, and willing to learn.