Top Machine Learning Jobs in London

Summer internship on the Global Data Science team to develop and optimize time-series forecasting models (ARIMA/SARIMA, XGBoost, LSTM). Support end-to-end data ingestion, back-test datasets, collaborate with traders and analysts, and conduct ad hoc research on energy fundamentals and analytics trends.

Lead commercial expansion into the UK Air domain, owning sales, pipeline, bids, contracts and account growth. Build senior customer and partner relationships, translate AI/ML capability into operational outcomes, and ensure solutions are deliverable and strategically aligned while working with multidisciplinary teams in secure defence contexts.

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 in computer vision and spatial AI, developing novel algorithms (metric learning, 3D geometry, vision-language) and moving prototypes to production-grade, real-time systems. Shape research roadmap, publish at top venues, mentor junior researchers, and collaborate with engineering to deliver deployable spatial intelligence.

Design, build, and deploy ML-powered services, APIs, microservices, and full-stack tools for fraud and identity analytics. Integrate models into real-time production, implement automated training/monitoring workflows, own DevOps and security for services, and collaborate with data scientists, architects, and QA.

Develop ML systems for autonomous driving, lead projects using RL and deep learning, and collaborate with cross-functional teams to improve Waymo's technology.

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.

Build and operate production GenAI infrastructure for open-weight LLMs and VLMs: real-time GPU serving, high-throughput batch inference, fine-tuning, autoscaling GPUs, backend services, observability, cost/latency optimization, and platform surfaces (LLM/Agent Gateways, evals, guardrails).

Design, train, evaluate and deploy production ML models for personalization and recommendations. Own end-to-end ML problems from data exploration and feature engineering to model serving, monitoring, and iteration while applying MLOps best practices and responsible AI principles.

Design, prototype, and implement machine learning models for game personalization. Convert prototypes into production-quality code, deploy via MLOps practices, and run A/B tests. Monitor player data, iterate models to improve retention and revenue, and document/share results across product and studio teams.

Lead the development of AI/ML infrastructure for simulations, collaborating with teams on model scaling, system design, and mentoring junior engineers.

Lead development of machine learning models, enhance user feedback analysis, drive technical direction, and optimize evaluation systems for autonomous vehicles.

Transform operational data into actionable insights by building semantic models, reporting solutions and metrics (Power BI/Fabric, DAX, M, SQL). Ensure data quality, governance, access controls, troubleshooting, documentation, and stakeholder collaboration to enable self-service analytics.

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.

Own and operate end-to-end ML infrastructure: build and maintain Airflow pipelines, CI/CD and IaC for model training/deployment, manage MLflow registry, deploy to AWS and edge devices, implement monitoring/data-versioning, ensure HIPAA/SOC2 compliance, and support reproducible, reliable ML in production.

Research, prototype, and deploy ML models for physiological sensing (radar, audio, other signals). Design experiments, implement production-grade models for cloud and on-device inference, optimize for constrained compute, collaborate with clinical and engineering teams, and contribute documentation and regulatory evidence.

The Staff ML Performance Engineer optimizes ML inference for edge devices, profiles performance bottlenecks, and implements improvements across systems while collaborating with model developers to enhance on-device performance.

The Senior ML Systems Engineer will build and maintain the training framework for large-scale language models, focusing on distributed training and performance optimization.

Lead and grow a team of ML scientists to build and productionise credit underwriting, pricing, limits, and lifecycle models. Define Borrowing's ML roadmap for UK growth, new segments, and EU expansion, improve feature engineering and data use, and raise standards for model quality, governance, and iteration speed.

As a Senior Machine Learning Engineer, you will lead the development of AI systems, design production-grade ML software, and mentor junior engineers while engaging with clients on technical strategies.

Lead design, build and deploy production-grade ML systems and infrastructure. Make architecture decisions, implement best practices for ML at scale, advise clients, collaborate cross-functionally, and mentor junior engineers while meeting operational and ethical standards.

Lead design and roadmap for a scalable ML/GenAI platform: lifecycle architecture (training, deployment, monitoring), cloud-native GPU infrastructure, CI/CD for ML, observability and governance, LLM/vector retrieval capabilities, self-service tooling, cross-functional alignment, and mentoring senior engineers to accelerate safe, cost-efficient production ML at company scale.

As an ML Solution Consultant, you'll guide R&D teams in using ML tools, ensuring effective data structuring, and maximizing the value of scientific data.

The role involves optimizing ML model performance, focusing on efficient training, low-latency inference, and understanding low-level GPU and networking technologies.