Top Machine Learning Jobs in Belfast

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 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.

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.

Create clear, customer-focused documentation and help content for cloud-based video security, access control, and intrusion platforms. Design user-centric guides, peer-review content for quality and consistency, and collaborate with Engineering, Support, Escalations, and UX teams to improve documentation and product usability.

Own commercial strategy and operations for enterprise pharma and AI accounts: drive pilots-to-revenue, account expansion, pipeline management, pricing, forecasting, and deal strategy. Build cross-functional relationships, push deals through legal/procurement/IT, act as CEO counterpart to shape strategy and product roadmap, and be highly present in person with customers and at conferences.

Lead end-to-end research applying advanced ML/DL (transformers, GNNs, RL, ensembles) to generate alpha across digital asset markets, build production-grade pipelines, prevent leakage/overfitting, analyze microstructure and on-chain data, and mentor junior researchers.

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.

Lead ML strategy and delivery for Workforce Management: forecasting, routing, assignment, escalation and time-to-completion prediction. Build, ship and maintain production ML systems, coach and grow a team of ML scientists, work cross-functionally to turn operational problems into pragmatic ML solutions, and improve customer and operational outcomes.

Lead Vegetation Intelligence product, owning roadmap from ML detection models to customer-facing risk products. Advance detection models, integrate outputs into outage and ignition risk, run user research, and translate model capability into actionable B2B products used in the field.

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.

Lead data visualisation and reporting strategy: design interactive dashboards, standardise metrics, modernise executive reporting, embed visualisations into SaaS products, develop data storytelling frameworks, translate complex analytics into actionable narratives, and train teams to drive insight adoption across the organisation.

Designs and deploys supervised machine learning classifiers using curated patent data, conducts desk research on technologies, scopes and defines client classifier taxonomies, supports customer meetings and data review, and delivers ad-hoc analysis and presentations as projects progress.

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.

The Senior Machine Learning Engineer will design and implement ML pipelines, optimize LLM performance, and collaborate on AI infrastructure for cost-efficient solutions.

The role involves building end-to-end production-grade ML systems, optimizing pipelines, integrating models into applications, and maintaining data systems.

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.

The Senior Machine Learning Engineer will build core ML systems, handle data preparation and iteration, debug model issues, and ensure reliable production performance while mentoring other engineers.

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.

Design, build, and maintain production-grade ML systems for revenue management. Own end-to-end ML lifecycle: data pipelines, model deployment, monitoring, testing, and scaling. Collaborate with product and engineering to define SLIs/SLOs, optimize pricing strategies, and mentor cross-functional teams to ensure reliable, high-quality ML-driven features.

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 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.