Top Machine Learning Jobs in Belfast

Leads the mid-market business development function, including team building, territory strategy, prospecting, negotiation, closing, pipeline management, and account acquisition and retention across pharma and biotech. Partners with marketing, strategy, and commercial operations on go-to-market execution. Represents the company at industry events and develops relationships with clinical trial sponsors while selling complex AI-powered SaaS solutions.

Leads a 25+ person organization of data scientists, machine learning engineers, and analytics engineers across Identity and Financial Crime. Owns data quality, governance, lineage, observability, fraud and financial-crime models, production performance, monitoring, retraining, and domain data products. Partners with engineering, product, risk, compliance, legal, and central data teams; influences upstream teams, develops managers and senior ICs, handles incidents, and scales hiring and technical strategy.

Build and deploy production machine learning systems for classification, retrieval, ranking, personalization, and evaluation. Develop training and inference pipelines, improve model quality and efficiency, create evaluation datasets and metrics, monitor production performance, debug issues, and ship reliable ML-powered features. The role emphasizes balancing accuracy, relevance, latency, cost, privacy, and reliability while collaborating with engineering and product teams.

Own and build Oversee’s product marketing function, including ICP definition, positioning, persona messaging, competitive and win/loss research, product content, sales enablement, and end-to-end product launches. Partner closely with product, sales, growth marketing, and executives to translate technical AI and travel technology capabilities into compelling commercial messaging, establish repeatable GTM processes, and measure launch performance.

Develop statistical and machine learning models using soccer tracking and event data to support player recruitment decisions. Build spatio-temporal models, metrics, analyses, and interpretation tools; validate models; communicate findings to professional club clients; and partner with engineering on cloud deployment and production reliability. The role also includes code review, model maintenance, and advancing analytical methods.

Design, build, deploy, and optimize scalable AI and machine learning systems using large datasets. Develop predictive models, recommendation systems, personalization engines, forecasting solutions, generative AI applications, and production ML pipelines. Collaborate with data, backend, and product teams to integrate models into applications, while monitoring performance, data quality, drift, reliability, and cost. Establish best practices for evaluation, experimentation, reproducibility, and deployment, and research emerging AI technologies.

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.

Perform malware and threat research across the attack lifecycle, validate and improve Falcon detections, collaborate with engineering and ML teams, automate analysis, support external security testing, and translate findings into product protections.

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.

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.

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.

Leads positioning, messaging, go-to-market strategy, launches, sales and channel enablement for Sophos AI cybersecurity solutions. Develops category narratives, launch assets, competitive intelligence, analyst relationships, customer feedback programs, and partner enablement. Collaborates cross-functionally with product management, research, sales, marketing, and channel teams to translate technical capabilities into differentiated market stories and pipeline outcomes.

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.

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.

Design and develop cloud-based data platform solutions, including data pipelines, ingestion, transformation, cleansing, integrations, and data applications. Translate technical requirements into detailed designs, write reliable code, develop test automation, troubleshoot operational issues, tune batch and real-time processing, secure cloud data platforms, and implement CI/CD and monitoring. The role also involves advising client developers and defining efficient operational processes.

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 end-to-end machine learning systems across data, training, fine-tuning, evaluation, inference, deployment, and monitoring. Build scalable GPU-based infrastructure and reliable production pipelines for large models, optimize performance and cost, and partner with research and application engineering to translate model capabilities into product improvements. Provide hands-on technical leadership, resolve production issues, and ensure measurable, safe, and maintainable ML iterations.

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.