2,251 to 2,275 of 2,743 Machine Learning Jobs in London

Databricks Platform (or demonstrated ability to achieve proficiency rapidly) with a developing technical specialisation in one area (e.g. real-time/streaming, ML/AI, data governance, migrations) Ability to contribute to architecture discussions with senior technical stakeholders: whiteboarding, design reviews, and trade-off analysis Track record of driving platform … Science, Engineering, or a quantitative discipline (or equivalent experience) Willingness to travel up to 30% as needed Nice to Have Databricks certifications (Data Engineer, ML, Platform) Cloud provider certifications at professional level (e.g. AWS Certified Solutions Architect – Professional or AWS Certified Data Engineer; Azure Solutions Architect Expert; Google Professional Cloud ...

Technical Oversight End-to-End Data Programme Delivery: Drive the full lifecycle of complex data initiatives (Data Lakehouses, Data Mesh, Real-time Streaming, and ML/GenAI pipelines) from inception to production, ensuring alignment with GlobalLogic’s Delivery Framework and client governance. Architecture & Technical Alignment: Bridge the gap between … Data & AI Architecture Fluency: Deep understanding of modern data stacks (Data Lakehouses, Data Mesh, Real-time Streaming, ETL/ELT pipelines) and AI/ML lifecycle management (MLOps, GenAI integration, Model Governance). Ability to challenge architectural patterns and supervise technical teams without acting as the hands-on architect. Cloud ...

Backend Software Engineer (IC3) to join our AI & Data Products squad and help build the next generation of product features powered by AI, ML, and intelligent automation. This is a hands-on backend role focused on turning predictive models, LLM capabilities, and business intelligence into real product experiences. You will … into Spendesk's user journeys, helping reduce manual work and make our product more proactive and intelligent. You will collaborate closely with the 3 ML Engineers in the squad, as well as Product Managers, Designers, and applicative squads across Spendesk. Together, you will build production-grade services that expose ML ...

inference tasks. You will develop production-ready pipelines for at-scale data processing using distributed computing frameworks, run and monitor large-scale deep learning experiments, and leverage JAX to establish end-to-end pipelines for fine-tuning foundational models. Your work will involve delivering synthetic datasets for key Android … more of the following: speech/audio (e.g., technology duplicating and responding to the human voice), reinforcement learning (e.g., sequential decision making), ML infrastructure, or specialization in another ML field. 1 year of experience with ML infrastructure (e.g., model deployment, model evaluation, data processing, debugging). Preferred qualifications: Master ...

inference tasks. You will develop production-ready pipelines for at-scale data processing using distributed computing frameworks, run and monitor large-scale deep learning experiments, and leverage JAX to establish end-to-end pipelines for fine-tuning foundational models. Your work will involve delivering synthetic datasets for key Android … more of the following: speech/audio (e.g., technology duplicating and responding to the human voice), reinforcement learning (e.g., sequential decision making), ML infrastructure, or specialization in another ML field. 1 year of experience with ML infrastructure (e.g., model deployment, model evaluation, data processing, debugging). Preferred qualifications: Master ...

Staff Product Manager, with a focus in Analytics, Experimentation, and CLV/LTV modelling, to lead the strategy, prioritization, and execution of AI/ML capabilities and products that drive business decision-making. This role operates at the intersection of business, product, data science, and engineering, and is responsible … leading high-impact problem spaces that span teams, while improving the effectiveness, consistency, and scalability of analytics product management practices. This includes technical ML solutions that help the business understand where player value is coming from and how it changes over time. The role is also responsible for creating ...

more of the following: speech/audio (e.g., technology duplicating and responding to the human voice), reinforcement learning (e.g., sequential decision making), ML infrastructure, or specialization in another ML field. 1 year of experience with ML infrastructure (e.g., model deployment, model evaluation, data processing, debugging). Preferred qualifications: Master … inference tasks. You will develop production-ready pipelines for at-scale data processing using distributed computing frameworks, run and monitor large-scale deep learning experiments, and leverage JAX to establish end-to-end pipelines for fine-tuning foundational models. Your work will involve delivering synthetic datasets for key Android ...

Software Engineer to join our AI Libraries team. This team builds and maintains the platforms, libraries, and tools that enable Wayve’s ML engineers and researchers to train, evaluate, and scale models efficiently. This is a hands-on software engineering role focused on building stable, scalable, and modular systems that … support large-scale ML development. You’ll work closely with ML teams across Wayve to understand their needs, design reusable abstractions, and improve the reliability, performance, and usability of our training infrastructure. You’ll play a key role in maturing Wayve’s AI platform and helping bring autonomous driving technology ...

high-ownership role working centrally across the business. You'll partner with teams spanning Commercial, Marketing, Product, Finance, Engineering and Operations — developing and deploying ML and AI solutions that drive outcomes across both sides of our marketplace. The problems you'll work on are genuinely varied: pricing models, propensity … expected to deliver them end-to-end, from first principles through to being deployment-ready and beyond. WHAT YOU'LL BE DOINGEnd-to-End ML & AI Delivery: Lead data science initiatives from problem framing through to deployment, monitoring, and iteration — delivering the full production lifecycle. With no dedicated ML engineering ...

with pricing or actuarial model deploymentFamiliarity with MLOps concepts (CI/CD pipelines, model registries, automated testing) — awareness required, not hands-on ownershipExperience with ML lifecycle tooling — MLflow, Azure ML, or equivalentPython proficiency — able to read and review analytical code, contribute where neededConsulting or forward-deployed engineering background (Palantir, QuantumBlack … leave plus ability to purchase 5 additional days, hybrid working options, Private Medical cover, Employee Share Purchase Plan, Life Assurance, Subsidised gym membership, Comprehensive Learning & development offerings, Employee Assistance program. Integrity. client focus. respect. excellence. teamworkOur core values dictate how we live and work. We're an ethical ...

test, and maximise the value of models ranging from LLMs to propensity and classification. Contribute to the end-to-end development and deployment of ML and AI models to prove and deliver tangible commercial value. Act as a technical bridge between Data Science and MLOps to embed best practice deployment … maximum impact. We're looking for someone who is: An experienced practitioner: You have 5+ years of experience as a Data Scientist or ML Engineer, covering both classic ML and Generative AI. A proven builder: You've successfully created multiple Data Science products that have moved the needle. A deployment ...

test, and maximise the value of models ranging from LLMs to propensity and classification. Contribute to the end-to-end development and deployment of ML and AI models to prove and deliver tangible commercial value. Act as a technical bridge between Data Science and MLOps to embed best practice deployment … maximum impact. We're looking for someone who is: An experienced practitioner: You have 5+ years of experience as a Data Scientist or ML Engineer, covering both classic ML and Generative AI. A proven builder: You've successfully created multiple Data Science products that have moved the needle. A deployment ...

allied integration, shaped by real-world frontline experience, and focused on protecting democratic societies at speed. About the role The Staff Engineer, AI/ML — Self-Serve Toolchain will build the end-to-end system that lets customers adapt UFORCE models on their own private data without exposing that data … flight principal-led capability into a repeatable toolchain that non-expert customers can run safely on-site. What you'll do Own the ML adaptation pipeline from raw customer data to trained, evaluated, deployable models. Build foundation-model-assisted labeling workflows using tools such as SAM-2 , Grounding DINO , open ...

enjoy turning ambiguity into reliable production systems, you’ll thrive here. Join a team that values craft, security, and learning. As an Applied AI ML Lead in LLM Suite Engineering, you will design and deliver production‐grade AI/ML and agentic solutions that integrate seamlessly with existing systems. … algorithms that integrate with existing systems Create architecture and design artifacts for complex applications, ensuring design constraints are met through delivery Build AI/ML solutions and agentic systems for the LLM Suite platform using public cloud architecture (Azure, AWS) and modern agentic frameworks Implement GenAI services leveraging Azure OpenAI ...

Microservices architecture, including containers and serverless implementation. E.g. Kubernetes, Docker, OpenShift, AWS Lambda, Cloud Functions (GCP).Machine learning tools and techniques, e.g. Auto ML, Tensorflow, Natural Language, Vision AIPA's common technology components: Google CloudCloud RunCloud FunctionsKubernetes Engine, Compute Engine, VPCsCloud Spanner, Cloud SQL and BigQueryDataflow, Apache BeamVertex ...

shaping the future of fintech – and we’re just getting started. There are a myriad of opportunities to use AI/ML as part of business processes in checkout, and we’re looking for an expert to help us make these a reality. Unlike many such roles, this … opportunity to truly drive innovation at scale that matters. We’re looking for a Staff Level AL/ML engineer to accelerate our adoption into the AI era; helping us set our AI vision and show us what is possible. As part of the Data and AI platform team ...

safety metrics) and online monitoring (user feedback, drift detection) Technical Skills & Experience 6-8+ years of hands-on experience building production AI/ML systems, with 3-4+ years deploying LLM applications to production Multimodal AI expertise practical experience building voice agents, vision systems, or conversational AI serving … MLOps & deployment hands-on with Docker, Kubernetes, CI/CD pipelines, and infrastructure-as-code Cloud platforms experience with AWS, Azure, or GCP for ML workloads and infrastructure management Exceptional communication ability to explain complex AI concepts clearly to both technical and business stakeholders Customer-facing experience in Solutions Architecture ...

enterprise customers navigating complex analytics modernisation challenges, whether migrating from legacy on‐premises data warehouses, building unified data platforms, implementing streaming analytics, or operationalising ML‐ready data foundations. You’ll translate ambiguous business requirements into scalable, cost‐effective analytics architectures that accelerate time‐to‐insight. You will join a team … provide customer feedback to analytics product teams, participate in private betas, and shape future service capabilities. Collaborate across specialist teams — work with AI/ML, Security, and Migration specialists to connect analytics foundations to downstream ML and GenAI workloads. About the team We’re the AWS Analytics Specialist Solutions Architecture ...

enterprise customers navigating complex analytics modernisation challenges whether migrating from legacy on-premises data warehouses, building unified data platforms, implementing streaming analytics, or operationalising ML-ready data foundations. You'll translate ambiguous business requirements into scalable, cost-effective analytics architectures that accelerate time-to-insight. You will join a team … roadmaps — provide customer feedback to analytics product teams, participate in private betas, and shape future service capabilitiesCollaborate across specialist teams — work with AI/ML, Security, and Migration specialists to connect analytics foundations to downstream ML and GenAI workloadsAbout the teamWe're the AWS Analytics Specialist Solutions Architecture team ...

market operates. This is not a research role — we need someone who ships. What You Will Do Lead the design and delivery of complex ML and GenAI systems — robust, scalable, and built for production. Guide the team across the full AI lifecycle, from experimentation and evaluation through deployment and monitoring … cash flow waterfalls, ABS/CLO structures, and priority of payments. Strong software engineering skills (Python preferred) combined with deep knowledge of AI/ML frameworks, GenAI tooling, and modern data infrastructure. Hands‐on experience building and deploying ML/GenAI systems in production within finance, legal, or other high ...

systems for text, image, speech, and video applications. Own end-to-end delivery, performance, and continuous improvement of LLM Suite products. Working closely with ML Engineering, Product Management, and Cloud Engineering, you will ensure our AI solutions are reliable, secure, and built for real business impact. Bridge advanced AI research … Skills: Hold a PhD or possess equivalent experience in Computer Science, Mathematics, Statistics, or a related quantitative discipline. Demonstrate extensive hands-on experience in ML engineering, with a proven track record of shipping production AI systems. Bring deep expertise in NLP, Computer Vision, and/or Multimodal LLM algorithms, with ...

equivalent research/industry experience Demonstrable hands-on experience building and deploying document understanding systems, information extraction pipelines, or knowledge graph construction using deep learning, LLMs and NLP methods Proven ability to translate complex document understanding problems into innovative AI applications that balance accuracy and efficiency Professional experience scaling … efficiency optimization including knowledge distillation, model compression, and designing SLM-based solutions that balance performance with computational constraints Solid understanding of DL/ML approaches used for NLP tasks Experience designing annotation workflows, creating high-quality labeled datasets with clear guidelines, and developing evaluation frameworks for document understanding tasks #LI ...

permanent talent and contractors. Mentorship: Coach existing team members through process updates and modern cross-functional ways of working. Skills & Qualifications Data Science & ML Expertise: Strong academic background (e.g., Master’s degree in Data Science or a scientific discipline with significant ML focus) alongside practical data science experience. Software Engineering ...

Production Solution Delivery: Lead impactful customer technical projects by delivering production-grade systems, designing and building reference architectures, custom applications and data ingestion and ML/AI model integration Transformational Impact: Guide strategic customers as they implement transformational big data projects, including end-to-end design, build and deployment … with distributed computing with Apache Spark and knowledge of Spark runtime internals Familiarity with CI/CD for production deployments Working knowledge of MLOps, ML/AI models and AI APIs Design and deployment of performant production end-to-end data architectures and applications that combine data pipelines, ML/ ...

with Bachelor's degree in computer science or a related discipline, or equivalent work experience required; advanced degree preferredStrong hands-on experience with Python, ML frameworks, and LLM/GenAI tooling. Strong hands-on experience with AI Development tools like Claude CLI, WindsurfSolid understanding of the ML lifecycle; exposure ...