Top Machine Learning Jobs in Bristol

Designs, builds, integrates, tests, and deploys scalable AI and cloud solutions for clients. Leads discovery, architecture, feasibility assessments, proof-of-concepts, bids, and initial delivery increments. Develops APIs, integrations, and data pipelines; evaluates ML and LLM technologies; and advises stakeholders on responsible AI adoption. The role requires strong software engineering, architecture, client engagement, communication, and technical ownership skills across pre-sales and implementation.

Lead EMEA communications strategy for a global B2B technology client. Develop integrated, social-first plans connecting paid, owned, earned, influencer, partnership, and offline activity. Translate complex product narratives into customer-focused messaging, creative briefs, presentations, and campaigns. Manage regional and global stakeholders, agencies, and production partners while supporting execution, localization, performance optimization, and brand consistency. Use GenAI and automation to accelerate creative development and improve marketing workflows.

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.

Lead implementation of production-grade ML systems: data pipelines, training/evaluation workflows, scalable GPU inference, model fine-tuning (LoRA/QLoRA/SFT/DPO), deployment, and monitoring. Collaborate with product and application teams to ship reliable, efficient, and safe ML-powered features for real users while managing latency, cost, and robustness constraints.

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

Lead technically complex product work on Rosso, embedding with ML, data science and engineering teams to turn business and customer needs into shipped features. Define success criteria for models/forecasts, manage edge cases and operational risk, and prioritize work across portfolio, forecasting, pricing optimisation and related systems.

Reviews and evaluates engineering, infrastructure, and platform documents and work products for technical accuracy, completeness, clarity, consistency, and relevance. Provides structured feedback, identifies errors and gaps, compares technical approaches, and follows defined evaluation frameworks. Requires independent work, strong communication, analytical ability, and professional engineering judgment. The role is fully remote and supports varied technical evaluation projects as an independent contractor.

As a Machine Learning Engineer, you will deploy AI/ML systems, translate client needs into solutions, and engage with users to provide support and improvements.

Own the full lifecycle of machine learning features, including research, experimentation, training, deployment, monitoring, and production optimization. Build ML infrastructure for payment routing, establish reliable MLOps practices, partner with product, data, engineering, and customers, mentor engineers, and guide technical priorities. Apply statistical thinking, A/B testing, pragmatic model evaluation, and reinforcement learning concepts in a remote-first environment.

Own the full lifecycle of machine learning features, including research, experimentation, training, deployment, monitoring, and production infrastructure. Build smart payment-routing models and MLOps practices, partner with product, data, engineering, and customers, evaluate outcomes statistically, mentor engineers, and help define ML technical direction and reliable development processes.

Own an AI stockbroking product vertical end-to-end, including strategy, metrics, roadmap, technical design, delivery, and launch. Partner with engineering on APIs, data flows, ledgering, security, scalability, and failure handling. Integrate KYC, transaction monitoring, privacy, licensing, and regulatory requirements. Apply AI with evaluation, safeguards, and human oversight while coordinating Engineering, Design, Compliance, Operations, Treasury, and Finance.

Own an AI investing product vertical end-to-end, including strategy, roadmap, technical design, delivery, metrics, and launch. Partner with engineering on APIs, data flows, ledgering, reconciliation, security, scalability, and failure handling. Embed regulatory requirements across KYC, monitoring, limits, privacy, and licensing. Apply AI with evaluation, safeguards, human oversight, and reliability controls while aligning compliance, operations, finance, treasury, and design.

Owns an AI neobank product vertical end-to-end, including strategy, roadmap, technical design, delivery, regulatory requirements, AI evaluation, reliability, and launch. Partners with engineering, compliance, operations, treasury, finance, and design across payments, remittance, insurance, wealth, and trading products. Must balance correctness, scalability, security, user experience, cost, speed, and regulatory constraints in high-consequence financial systems.

Owns roadmap execution and capability definition for an Agentic AI Platform. Translates AI platform strategy into requirements, prioritized roadmaps, reusable agent orchestration and lifecycle capabilities, governance controls, observability, and scalable platform patterns. Partners with engineering, architecture, data, security, legal, compliance, customer success, professional services, and customers. Measures adoption, reliability, safety, quality, cost, and business impact while balancing technical feasibility, security, compliance, and customer value.

Build and extend the platform layer: design shared backend services and APIs, improve reliability and correctness, collaborate with product teams to remove platform bottlenecks, mentor engineers, shape architecture and migrations, and write high-quality fullstack code with a backend focus.

Own the end-to-end product direction and quality of AI-powered email capabilities. Define requirements, evaluation frameworks, quality standards, and measurable outcomes while balancing model quality, latency, cost, reliability, safety, and user experience. Partner closely with ML, backend, and client engineers on system design, experimentation, failure handling, and iteration. Drive prioritization, technical decisions, execution, and user trust in a fast-moving zero-to-one environment.

Own the end-to-end product strategy and execution for an AI-powered email application. Define AI behavior, system requirements, evaluation frameworks, quality standards, and user experiences while balancing reliability, latency, cost, safety, and usability. Partner closely with ML, backend, and client engineering teams to identify failure modes, improve system performance, prioritize work, and deliver predictable, trustworthy AI capabilities in a fast-moving zero-to-one environment.

Own the end-to-end product direction and quality of an AI-powered email application. Define AI behavior, system requirements, evaluation frameworks, quality standards, and user experiences while balancing quality, latency, cost, reliability, safety, and usability. Partner closely with ML, backend, and client engineers to identify failure modes, prioritize improvements, run experiments, and deliver predictable, trustworthy AI features in a fast-moving zero-to-one environment.

Owns the end-to-end definition, evaluation, and quality of AI-powered email capabilities. Partners with ML, backend, client, and product teams to translate model behavior and technical constraints into requirements, system decisions, experiments, and roadmaps. Balances quality, latency, cost, safety, reliability, and user experience while identifying failure modes and building trustworthy, predictable AI features.

The Technical Account Manager will drive customer success, manage accounts, deliver onboarding and demos, and align engineering solutions with customer needs.