Job Title: Senior Software Engineer, GenAI Platform Company: Deliveroo Job Category: Machine Learning Roles & Responsibilities: Deliveroo is now part of DoorDash, bringing together teams with even greater reach, scale, and ambition. Depending on your role, you may collaborate with teammates, systems, and leaders across DoorDash and Wolt. Together, we're unlocking new possibilities as one global team. About the Role ------------------ You will join a small, high\-leverage team building production infrastructure for Generative AI at Deliveroo and DoorDash, leading the design and architecture of our open\-weights model platform spanning inference and fine\-tuning: real\-time GPU serving, high\-throughput batch inference, and model fine\-tuning. You'll set technical direction across model serving and inference engines, fine\-tuning and training pipelines, GPU autoscaling and utilization, batch pipelines, backend services, and observability, and mentor engineers as you go. This role is ideal for a senior engineer who enjoys owning ambiguous, high\-impact systems and pushing the cost/performance frontier of GPU inference and fine\-tuning in a fast\-moving technical area where product needs, model capabilities, vendor ecosystems, and cost/performance tradeoffs are evolving quickly. * Lead the design of infrastructure that helps teams move GenAI ideas from prototype to production, increasing the velocity of business impact from AI across the company. * Own and evolve our open\-weights serving stack — real\-time GPU endpoints, high\-throughput batch inference, and fine\-tuning (SFT/DPO/LoRA) — alongside the LLM Gateway, Agent Gateway, evals infrastructure, guardrails, and cost attribution. * Architect scalable, high\-performance systems for model serving, batch inference, GPU autoscaling, and fine\-tuning that power real customer and internal automation use cases * Push the cost and latency frontier of GPU inference — turning batch jobs that took days into hours and cutting inference cost by multiples — while giving product teams a clean choice across open\-weight and closed\-source models with reliability, fallback, observability, and cost controls built in. * Build platforms that support rapid experimentation while meeting production standards for latency, scale, monitoring, SLOs, playbooks, and operational excellence. * Partner closely with — and raise the technical bar for — ML engineers, product engineers, data scientists, and platform teams across DoorDash, Wolt, and Deliveroo to turn emerging GenAI capabilities into durable platform primitives. * Set technical direction for the future of the company's centralised GenAI platform — including emerging directions such as reinforcement learning (RLHF/RLVR), agent optimisation, and other post\-training and agentic techniques — enabling the next generation of AI\-powered products, agents, automation, and personalisation. * BSc, MSc or PhD in Computer Science or equivalent * 5\+ years of industry experience in software engineering * Deep backend engineering fundamentals, especially in Python and distributed systems. * Track record of designing and owning production services, APIs, data pipelines, or ML infrastructure at scale. * Experience operating systems in production, including observability, debugging, reliability, incident response, and performance/cost optimization. * Deep hands\-on experience with LLM inference and/or fine\-tuning of open\-weight models in production — serving (latency, throughput, batching, autoscaling, GPU utilization) and/or fine\-tuning (SFT/DPO/LoRA). * Demonstrated technical leadership: leading design across ambiguous, fast\-moving technical areas, mentoring engineers, and turning customer use cases into reusable platform capabilities * Proficiency in using AI coding tools (e.g., Claude Code, Codex, Cursor) in the full software development lifecycle, including designing, generating code, testing, monitoring and releasing software If you have a disability or long\-term health condition and need support to apply for one of our roles, or require any reasonable adjustments during the recruitment process, you'll have the opportunity to let us know once you've submitted your application. We'll share details on how to request support so we can ensure you have a fair and equitable experience. If you're excited about making a real impact in a fast\-moving marketplace and growing your career alongside ambitious, supportive teams, we'd love to hear from you! Work Location: London, ENG, UK Availability: Full time

Job Title: Director, AI and Machine Learning Engineering Company: Bain & Company Job Category: Machine Learning Roles & Responsibilities: You will be part of a team building Bain’s integrated suite of agentic decisioning tools that gives large organizations always\-on intelligence across their most important growth decisions. The role sits at an intersection few organizations can offer: the analytical depth and client access of a top\-tier consultancy, the technical ambition of a product company, and engineering work at the frontier of applied AI. This is a startup inside an established company. You get genuine early\-stage ownership and real influence over architecture and product direction, backed by Bain’s global scale, client relationships, and leadership commitment; the pace of a startup with the runway of a global firm. The AI Engineer is a core builder of the Growth AI Performance System platform’s Agentic Core and Interface: the shared foundation that all three product modules are built on. This is not a module\-level role; you are building the central intelligence layer: the agentic workflows, retrieval and knowledge pipelines, tool integrations, and interface infrastructure that every product in the suite depends on. Moving solutions from early POC and MVP through to scaled production deployments, you are responsible for the reliability, quality, and coherence of the systems that real client teams use every day. The role requires both deep technical capability and genuine client awareness. You will engage directly with consulting teams and, at times, with senior stakeholders, presenting how the Agentic Core works, translating system design decisions into business terms, and using that feedback to make the platform better. The build loop here is tight: client engagement informs engineering, and every release makes the suite more capable. This role sits at the intersection of research and production engineering. The problems are rarely solved by off\-the\-shelf tooling: they require judgment about when agentic approaches add real value, how to build systems that fail gracefully, and how to measure quality in ways that matter to real users on real client engagements. * Build and own core components of the Growth AI Performance System Agentic Core, the shared intelligence layer that all product modules depend on, including multi\-agent orchestration frameworks, tool registries, shared memory and state management, and the interface infrastructure that surfaces platform capabilities to users * Build AI applications for enterprise use cases across the full delivery lifecycle, from rapid POCs and MVPs through to scaled production deployments, designing AI workflows that execute multi\-step tasks reliably and safely \- building evals to measure output quality, benchmark model performance, and validate reliability before and after deployment * Implement agentic patterns with strong judgment about when they add real value; build robust agent capabilities including context engineering, memory and state management, reasoning and planning, reflection, orchestration, routing, tool selection, function calling, and tool integration, with clear separation of concerns so the tool ecosystem scales cleanly * Design and build advanced retrieval and knowledge pipelines across diverse data structures (vector, keyword, hybrid, and graph\-based), covering embedding generation and optimization, indexing strategies, metadata design, relevance tuning, caching, access controls, and source attribution * Apply fine\-tuning and parameter\-efficient tuning techniques where they measurably outperform prompting and retrieval alone; run model experimentation and early\-stage research to inform architecture decisions * Integrate platform capabilities into enterprise environments, balancing quality, latency, cost, privacy, and adoption; build reusable components and accelerators that scale across all three product modules * Engineer for real delivery across the full software development lifecycle: write clean, testable, maintainable code; build APIs and microservices for cloud\-native deployment; implement MLOps, AIOps, and GenAIOps practices including CI/CD, reproducibility, environment parity, and versioning for models, prompts, and agents * Build evaluation and observability for the Agentic Core: tracing and instrumentation of agentic execution, regression test suites, automated scoring pipelines, and iteration loops for prompt and policy optimization * Design evaluation frameworks that track hallucination rate and factual consistency, relevance and retrieval precision/recall, latency and throughput, and cost per task; feed user signals and human feedback into continual improvement cycles and ongoing model updates * Apply data science and ML skills as needed to materially improve platform performance or reliability: data preparation, feature engineering, model selection, training, validation, and reproducible evaluation pipelines * Design for secure enterprise deployment: access controls, auditability, responsible handling of sensitive data, and responsible AI guardrails, including fallbacks, red\-teaming, and human\-in\-the\-loop (HITL) review; implement security, privacy, and governance requirements across all platform services; partner with Bain’s global Responsible AI and ethics teams to keep the platform aligned with firm standards * Engage directly with consulting teams and senior stakeholders to understand how the Agentic Core is being used, where it falls short, and what needs to change; translate that feedback into concrete engineering improvements * Present complex agentic system design, tradeoffs, and implementation decisions to senior stakeholders, including C\-suite and executive leadership, in clear, non\-technical terms that build confidence in the platform * Contribute to discovery sessions and solution shaping with case teams; operate as a credible, composed technical voice that connects engineering decisions to commercial outcomes; act as a thought partner on AI strategy, architecture choices, emerging capabilities, and implementation roadmaps * Drive innovation through out\-of\-the\-box thinking; participate in POCs and innovation workstreams to validate new tools and technologies; stay current with emerging AI and agentic trends and share learnings across the team * Partner closely with product, engineering, data science, ethics, and infrastructure colleagues to build robust, compliant AI systems * Mentor and upskill engineers and case team members on best practices in RAG, agents, prompt engineering, and AI safety * Strong proficiency in Python and experience building and integrating production services; hands\-on experience with LLM APIs, agentic orchestration frameworks, and deploying services on Azure or AWS * Experience designing and implementing agentic workflows and multi\-step LLM pipelines; deep familiarity with context engineering, memory and state management, tool integration, routing logic, planning and reflection, and orchestration patterns; working knowledge of ReAct, RLAIF, and other HITL and feedback\-loop techniques * Experience building retrieval and knowledge pipelines across vector, keyword, hybrid, and graph\-based data structures, including hands\-on use of vector and graph databases; understanding of embedding optimization, indexing strategies, relevance tuning, and how retrieval connects to LLM\-powered workflows * Proficiency with MLOps and AIOps practices: CI/CD for AI services, versioning for models, prompts, and agents, automated evaluation pipelines, and observability for agentic systems * Advanced prompt engineering, context engineering, and conversation design; strong grounding in evaluation design, experimentation frameworks, and data labeling strategies for LLM applications * Solid system design and architecture skills, with deep familiarity with cost optimization and computational tradeoffs for LLM workloads * Ability to apply data science and ML skills to improve system performance: feature engineering, model selection, training, validation, and reproducible evaluation pipelines * Strong engineering fundamentals: clean, testable, maintainable code; code review; version control; automated testing; and performance profiling * Native AI fluency in day\-to\-day engineering, active use of AI\-assisted coding tools (Cursor, Claude Code, or equivalent) to accelerate development and prototype faster * Demonstrated commitment to responsible AI practices: access controls, auditability, bias awareness, and the judgment to know when a system is not ready to deploy * 10 or more years in software engineering or applied AI roles with significant hands\-on building responsibilities * Track record of shipping production AI or ML systems end\-to\-end, from early experimentation through to live deployment * Experience contributing to complex, multi\-stack generative AI programs from conception through production, including mentoring other engineers along the way * Experience working in agile, cross\-functional product or consulting\-adjacent environments; strong troubleshooting and issue resolution skills; comfortable operating in high\-ambiguity environments * Direct experience engaging with clients or senior business stakeholders in a technical capacity, presenting, advising, or shaping solutions in a consulting\-adjacent environments is a strong advantage Work Location: London, ENG, UK Availability: Full time

Job Title: Senior/Lead Machine Learning Engineer Company: Mimica Job Category: Machine Learning Roles & Responsibilities: We’ve raised $26 million in Series B financing and are scaling fast. Backed by investors including Khosla Ventures and Paladin Capital, our proprietary AI technology is trusted by Fortune 500 companies to accelerate transformation. Mimica is recognized as a Top 100 AI Start\-up by Accel and an industry Leader by the Everest Group. Behind our platform is a team of exceptionally talented people, remote\-first and globally distributed across 16\+ countries. We're looking for curious, collaborative people excited to solve complex problems. You'll join a fast\-paced environment built on high ownership, bottom\-up thinking, and transparent, open communication – helping some of the world's leading organizations transform the way work gets done with AI. * We prioritize customer needs first * We work in small, project\-based teams * We have flexibility in terms of the problems we work on * We own the full lifecycle of our projects * We avoid silos and encourage taking up tasks in new areas * We balance quality and velocity * We have a shared responsibility for our production code * We each set our own routine to maximize our productivity * Own, debug, and improve production pipelines for accuracy and efficiency. * Design, build, and iterate on LLM evals and LLM\-as\-a\-judge systems, including the datasets, metrics, and benchmarks to monitor quality. * Run rigorous experiments on prompts, pipeline design, model choice, and inputs (e.g., OCR text vs. image \+ OCR), and turn the results into clear recommendations. * Drive LLM efficiency improvements across our pipelines by comparing models, restructuring steps, and replacing LLM calls with smaller, purpose\-built models where it makes sense. * Expand our anonymization pipeline, including evaluating and adapting existing models. * Work with Python and quality engineers to take experiments into production, and share responsibility for the code you ship. * Document findings in writing (eg. experiment reports, design docs) Annual learning \& development budget Work Location: London, England, UK Availability: Full time

Job Title: Student Researcher, 2027 Company: Google Job Category: Machine Learning Roles & Responsibilities: The Student Researcher Program fosters academic collaborations by hiring students onto research projects aligned to company priorities in scientific advancement. The program offers placements on teams across Google, for research, engineering, and science roles. As a Student Researcher, you will have the opportunity to participate in research projects focused on developing solutions for real\-world, large\-scale problems. Researchers across Google are working to advance the state of the art in computing and build the next generation of intelligent systems for all Google products. To achieve this, we invest in foundational research and work on projects that utilize the latest computer science techniques developed by skilled software developers and research scientists. Whether we're shaping the future of sustainability, optimizing algorithms, or pioneering AI systems, our teams strive to continuously progress science, advance society, and improve the lives of billions of people. Google Research is building the next generation of intelligent systems for all Google products. To achieve this, we’re working on projects that utilize the latest computer science techniques developed by skilled software developers and research scientists. Google Research teams collaborate closely with other teams across Google, maintaining the flexibility and versatility required to adapt new projects and foci that meet the demands of the world's fast\-paced business needs. Responsibilities -------------------- Work Location: London, ENG, UK Availability: Full time

Job Title: Machine Learning Engineer I Company: American Express Global Business Travel Job Category: Machine Learning Roles & Responsibilities: We are looking for a Machine Learning Engineer I to join our Product AI Engineering team. You will help us design, build, and optimize intelligent systems. This is not just a modeling role, you’ll own the full lifecycle: from raw data to deployed models / compound systems to measurable business impact. If you’re passionate about solving real\-world problems at scale, collaborating across disciplines, travel, and making AI systems that truly matter – this is for you. What You’ll Do: * Utilize AI techniques, including machine learning, deep learning, generative AI, and statistical modeling, to develop and implement solutions * Apply strong coding skills, analytical abilities, and innovative thinking * Employ statistical and data science methods to make data\-driven decisions. * Manipulate large data sets for business insights and drive actionable solutions. * Utilize appropriate methods and approaches to develop practical solutions for the business. * Structure work, frame issues, and produce analyses/ML models/Compound AI systems * Communicate complex data science topics in a clean \& simple way to multiple partners and senior leadership. * Collaborate with a global team across different continents to achieve project goals. * Flexible benefits are tailored to each country and start the day you do. These include health and welfare insurance plans, retirement programs, parental leave, adoption assistance, and wellbeing resources to support you and your immediate family. * Travel perks: get a choice of deals each week from major travel providers on everything from flights to hotels to cruises and car rentals. * Develop the skills you want when the time is right for you, with access to over 20,000 courses on our learning platform, leadership courses, and new job openings available to internal candidates first. * We strive to champion Inclusion in every aspect of our business at Amex GBT. You can connect with colleagues through our global INclusion Groups, centered around common identities or initiatives, to discuss challenges, obstacles, achievements, and drive company awareness and action. * And much more! Work Location: London, England, UK Availability: Full time

Job Title: Graduate Machine Learning Engineer Company: G-Research Job Category: Machine Learning Roles & Responsibilities: As part of our engineering team, you’ll shape the platforms and tools that drive high\-impact research \- designing systems that scale, accelerate discovery and support innovation across the firm. ### Role purpose ### Key Responsibilities of the role include * Implementing ideas from published research papers * Writing custom libraries to efficiently train models on petabytes of data * Reducing model training times by hand optimising machine learning operations * Profiling custom ML architectures to identify performance bottlenecks * Evaluating the latest hardware and software in the machine learning ecosystem Work Location: London, England, UK Availability: Full time

Job Title: Machine Learning Engineer Company: Hirivo Job Category: Machine Learning Roles & Responsibilities: You'll join a small applied\-AI team and own the ML behind Hirivo. That covers the job\-matching and ranking models, the LLM agents that read job adverts and fill application forms, and the systems that tell us whether they're working. You'll take features from prototype to production, then measure and monitor them. * Build and improve LLM\-powered agents that read job adverts and application forms and complete them accurately from a candidate's profile and CV (tool use, multi\-step workflows, handing questions to the candidate when needed). * Build job\-to\-candidate matching and ranking using embeddings, retrieval (RAG) and vector search, behind the campaign match score. * Create evaluation sets and automated evals for form\-fill accuracy, match quality and agent success rate, then iterate on prompts, models and fine\-tunes. * Train, fine\-tune and benchmark models (PyTorch), for example to parse CVs and job descriptions and to classify job fit. * Deploy models and agents as scalable APIs and services on the cloud (AWS/GCP) with Docker, Kubernetes and CI/CD, integrated with our Node backend. * Add observability: tracing of agent runs, latency/cost dashboards, failure and drift monitoring. * Build data pipelines that collect and clean job listings from many sources. * Work with product and with candidates to turn real job\-seeker problems into shipped features, and care about responsible AI: candidates' data, fairness and accuracy. * Building LLM evaluation frameworks (evals, benchmarks, LLM\-as\-judge). (40%)* * Monitoring and observability tooling (e.g. Datadog, OpenTelemetry, LangSmith). (55%)* * Open\-source contributions or a public portfolio of AI projects. (36%)* * A second language such as TypeScript, Go, C\+\+ or Rust. (TypeScript/JS 31%, Go 16%)* * GPU / CUDA / inference\-performance optimisation. (22%)* * Fine\-tuning (LoRA, RLHF/DPO). (18%)* * Familiarity with AI safety or responsible AI. (21%)* * Recommender/ranking systems, MLOps tooling (MLflow, model registry), web scraping/data pipelines. (13\-15% each)* Work Location: Hybrid remote in London Work Location: London, ENG, UK Availability: Full time

Job Title: Principal Engineer Company: moneyhub Job Category: Machine Learning Roles & Responsibilities: Moneyhub empowers leading financial services organisations to understand their clients’ needs better. We provide the data services and embedded technologies required to deliver highly personalised, lifelong financial relationships. The Role You will have seen approaches fail in different companies and codebases and you know why. People will want your view before they start something and you will have influenced what a team builds without being their manager. Success in this role looks like a steady record of shipped work, with at least two of your architectural patterns or AI standards in use across engineering. Our culture is based on trust, and we empower people to deliver in the way that suits them best. For this role, that means real say over how you solve problems and some latitude over what you pick up, alongside the expectation that your work is visible, shared with your colleagues and pointed at what the business needs. In practice, you might have a committed bet, something you are improving because it needs it, and something you are exploring for a future cycle. Judging which deserves your time, and being open about it, is a big part of the job. This is an individual contributor role. No line management, and no moving away from the code. * You cannot point to software that end users used, or to technical decisions of yours that people followed. * You want to stay in one language, or only the front end or the back end. * You are unsure about agentic engineering, or see it as a shortcut. * You only want a defined set of tickets. * You would rather work outside a team process than within one. * You want a management role, or architecture at a distance from the codebase. Questions about the role? Please contact Dilshan Rukunayake, Principal TA Partner, dilshan.rukunayake@moneyhub.com * 5% employer pension contribution from your first day, with a 3% employee contribution * 25 days’ holiday plus bank holidays, rising to 30 days after two years * Option to take UK bank holidays at other times, on days of significance to you * Holiday purchase scheme * Private medical insurance, including cover for pre\-existing conditions, plus dental and optical benefit * Three months’ Moneyhubber Family Pay when you become a new parent * Permanent health insurance and life cover well above the industry standard * Employee assistance programme * Life event leave * Professional development support, with a dedicated allowance of time and money * £750 towards professional memberships * Remote working support, including access to co\-working spaces and help setting up your home office * High\-spec laptop * Cycle to work scheme * EV car scheme Work Location: London, ENG, UK Availability: Full time

Job Title: Senior Machine Learning Engineer Company: UnderwriteMe Job Category: Machine Learning Roles & Responsibilities: Job Description As a Senior Machine Learning Engineer, you’ll take ownership of designing, building, and deploying advanced LLM\-based and agentic systems that extract and reason over medical records and application data. You’ll experiment, iterate, and ship quickly, working alongside a cross\-functional team in a dynamic, fast\-fail environment. You'll own the evals that prove your system works, and the guardrails that keep it working once real users depend on it. Your work will have a direct impact on our product and business as we push the boundaries of what’s possible with AI. What You Will Be Doing * Build \& Iterate Fast: Design, experiment, and deploy production LLM and agentic systems. Quickly test hypotheses, optimise, and iterate based on real\-world feedback and eval results. * Own Evals: Build and maintain the evaluation harness that tells us whether a model or prompt change made things better or worse. * Design Agentic Workflows: Architect multi\-step, tool\-using pipelines with explicit state, guardrails, and human\-in\-the\-loop review where the stakes demand it. * Lead Innovation: Push the envelope with cutting\-edge LLM and agentic techniques to solve hard problems in language understanding, extraction, and reasoning. * Shape the Product: Collaborate with Product, Design, and Underwriting SMEs to align ML capabilities with product goals and real domain constraints. * Scale \& Optimise: Take systems from prototype to production, optimising for latency, cost per run, and reliability at scale. What Type Of Person Fits The Role * Experience: 6\+ years in ML/AI, with deep hands\-on expertise in NLP, LLMs/SLMs, and agentic systems. * LLMs \& Architectures: Hands\-on experience building with OpenAI and Anthropic models/APIs; strong knowledge of LLMs and agent architectures. * Tooling: Practical experience with LiteLLM, vLLM, Langfuse (or equivalent LLM observability tooling). Experience with Claude Code/Codex and associated coding\-agent workflows is a plus. * Evals: Proven ability to design, build, and own evaluation harnesses for LLM/agent outputs. * Prompt \& Context Engineering: Proven ability to design and optimise prompts and context, including techniques like few\-shot, chain\-of\-thought, and prompt tuning techniques, grounded in measurable evaluation rather than guesswork. Prior experience with  DSPy, GEPA, or similar is a plus. * Backend \& Infra Skills: Strong Python skills, with FastAPI for service development, Postgres (or similar) for data persistence, and comfort with event\-driven, async architectures. Working knowledge of ML frameworks (PyTorch, TensorFlow), containerisation with Docker and Kubernetes, and cloud platforms (AWS preferred). * Think in Outcomes: You care about whether the system actually solves the underwriter's problem, not just whether the model scores well. You're comfortable challenging the spec, proposing a different approach, and owning your feature from idea through to production impact. * Mindset: Entrepreneurial, ownership\-driven, and biased to action, with a "Build Fast, Fail Fast" approach, thriving in agile, iterative environments. What We Offer Here at UnderwriteMe, you have the chance to work on solving complex problems that will change how lives are protected. This means working in an environment where you can really make a difference and where the opportunities for continuous learning and development are abound. We also take pride in giving back to the community and offer rewarding volunteering and social responsibility opportunities. Our Core Products Are * Decision Platform \- a B2B enterprise platform that provides a best\-in\-market Underwriting Rules Engine used by over 30 insurers to power their underwriting decisions. The product is sold across UK \& Ireland, Asia\-Pacific and North America. The platform combines our engine with technology to source and process third\-party information supporting underwriting decisions for insurers. We are already working on capabilities, powered by AI, to make these decisions even more efficient. * Protection Platform \- a B2B2C marketplace used by over 15,000 advisors to power quoting and purchase of protection products in the UK. We currently deliver 16% of the UK markets protection policies and we are already working to increase our reach. Every person in our global team is valued for the unique qualities they bring to our business and we seek to build their expertise and support their individual ambitions at every step. Of course, we take our work seriously and we know our team can operate under great pressure. We work hard and thrive on achievement, but we also know how to have fun and relax too. We regularly host a range of team building days to strengthen our team's connection with each other and reflect on their successes. You Can Be Who You Are We are committed to a culture of diversity and inclusion that embraces the authenticity of all employees, partners and communities. We support all employees to thrive and achieve their fullest potential. Work Location: London, England, UK Availability: Full time

Job Title: Applied AI ML - Senior Associate - Machine Learning Engineer Company: JPMorganChase Job Category: Machine Learning Roles & Responsibilities: Job Description Join a high performing team of applied AI experts to drive innovation and new capabilities in the Commercial \& Investment Bank. As an Applied AI / ML Senior Associate Machine Learning Engineer in the Applied AI ML team at JPMorgan Commercial \& Investment Bank, you will be at the forefront of combining cutting\-edge AI techniques with the company's unique data assets to optimize business decisions and automate processes. You will have the opportunity to advance the state\-of\-the\-art in AI as applied to financial services, leveraging the latest research from fields of Natural Language Processing, Computer Vision, and statistical machine learning. You will be instrumental in building products that automate processes, help experts prioritize their time, and make better decisions. We have a growing portfolio of AI–powered products and services and increasing opportunity for re\-use of foundational components through careful design of libraries and services to be leveraged across the team. This role offers a unique blend of scientific research and software engineering, requiring a deep understanding of both mindsets. Job Responsibilities * Build robust Data Science capabilities which can be scaled across multiple business use cases * Collaborate with software engineering team to design and deploy Machine Learning services that can be integrated with strategic systems * Research and analyse data sets using a variety of statistical and machine learning techniques * Communicate AI capabilities and results to both technical and non\-technical audiences * Document approaches taken, techniques used and processes followed to comply with industry regulation * Collaborate closely with cloud and SRE teams while taking a leading role in the design and delivery of the production architectures for our solutions. * Act as an individual contributor, though there will be optional opportunity for management responsibility dependent on the candidate's experience. About Us J.P. Morgan is a global leader in financial services, providing strategic advice and products to the world's most prominent corporations, governments, wealthy individuals and institutional investors. Our first\-class business in a first\-class way approach to serving clients drives everything we do. We strive to build trusted, long\-term partnerships to help our clients achieve their business objectives. About The Team J.P. Morgan's Commercial \& Investment Bank is a global leader across banking, markets, securities services and payments. Corporations, governments and institutions throughout the world entrust us with their business in more than 100 countries. The Commercial \& Investment Bank provides strategic advice, raises capital, manages risk and extends liquidity in markets around the world. Work Location: Greater London, England, UK Availability: Full time