174 jobs for “Machine Learning Engineer cambridge”
Medtronic London £76,800 – £115,200 pa
Designs, benchmarks and optimises deep learning models for computer vision and surgical video understanding, focusing on low-latency inference on edge and cloud devices. Works with ML, platform and embedded engineers on end-to-end video pipelines, using PyTorch, TensorRT and techniques like pruning and quantization. Distinct for its operating-room, safety-critical medical imaging context.
Helsing London
Architect and scale the deep learning frameworks and distributed training infrastructure behind defence AI systems, extending PyTorch-based tooling and optimising GPU throughput. The role spans data strategy, storage and orchestration, working cross-functionally to maximise training efficiency and developer velocity.
PhysicsX New York, United States US$150,000 – US$250,000 pa
Builds and deploys customer-facing applications on top of physics AI models, working directly with clients, data scientists and ML engineers. Involves full-stack work: FastAPI REST APIs and Python microservices, plus cloud deployment with Helm, Kubernetes, Terraform and AWS/Azure. Expect on-site travel to customer sites for 3-4 weeks per quarter.
PhysicsX Singapore
Customer-facing engineering role building and deploying applications that put physics AI models into production for industrial clients. You will work directly with customers, data scientists and ML engineers, prototyping tools, iterating on user feedback, and building Python FastAPI microservices deployed on Kubernetes, Terraform and AWS/Azure.
Anthropic London £165,000 – £190,000 pa
Pre-sales architect acting as a trusted technical advisor to large enterprises, helping them integrate and deploy Claude across their technology stack. Combines deep LLM expertise with customer-facing work, from technical discovery and evals to scalable architecture design, while feeding integration insights back to Product and Engineering.
The Portfolio Group Manchester
Set the AI vision and roadmap while staying hands-on, architecting and deploying machine learning and generative AI solutions into production. Work spans RAG, LLMs, vector search and knowledge graphs, plus building a cross-functional team and establishing AI governance. Based fully on-site in Manchester, reporting into senior leadership.
Thought Machine Lisbon, Portugal
Owns complex, ambiguous technical challenges end-to-end, shaping long-term platform architecture and company-wide engineering standards. Drives adoption of AI tooling, leads technical interviews, and mentors senior engineers while acting as a link between leadership and engineering teams. Based in the Lisbon office, requiring three days a week onsite.
Thought Machine Lisbon, Portugal
Builds end-to-end features across web applications and backend services, owning requirements from engineering design through task breakdown, implementation and production debugging. Works with React, TypeScript and REST APIs in a cross-functional team of backend, front-end and full-stack engineers, with automated testing integrated throughout the development lifecycle.
Palo Alto Networks London
Acts as "customer zero" and advocate for Identity Security, defining and validating enterprise architectures for machine identity, secrets governance and agentic identity management. Produces technical content such as blogs, whitepapers and deep-dive decks, builds reference architectures and demos, and partners with specialist sellers and solutions teams on complex customer engagements. Also feeds product insights and leads market education through executive briefings and conference talks.
Databricks London
Lead and scale the UK Forward Deployed AI Engineering team, delivering professional services engagements that help EMEA customers productionise GenAI and ML applications. The role combines people leadership, executive customer relationships, and cross-functional work with Product, Research and Sales to shape delivery models and the product roadmap.
Aioi Nissay Dowa Europe Oxford £81,322 – £112,488 pa
This role involves leading research in privacy-preserving generative AI, focusing on techniques like differential privacy, federated learning, and secure inference. The scientist will design and validate machine learning models, translate research into deployable systems, and contribute to publications and collaborations within a multi-partner environment. Emphasis is placed on responsible AI, reproducibility, and mentoring junior team members.
eFinancialCareers London
Lead the build-out of a hedge fund's machine learning function, defining research strategy and hiring a team of ML researchers and engineers. Directs work in deep learning, reinforcement learning, probabilistic and time-series modelling, and generative AI, partnering with quant and investment teams on alpha generation, forecasting, portfolio construction, risk and execution. Combines hands-on technical direction with strategic leadership.
Isomorphic Labs London
Translates Isomorphic Labs' security strategy into engineering delivery, steering a multi-quarter InfoSec roadmap spanning Cloud Security, Platform IAM and Secure SDLC. Partners directly with engineering and ML teams to unblock architectural issues, secure drug discovery pipelines and build data-driven dashboards giving the CISO visibility into program health and risk metrics. Reports to the CISO with a functional tie to the TPM Discipline Lead.
Isomorphic Labs London
Lead and contribute to core machine learning research applying large language models to drug discovery, developing and refining LLM-driven approaches across biology and chemistry problems. Work spans post-training, reasoning, test-time scaling, tool-use, alignment, agents, reinforcement learning and fine-tuning, with model training and inference engineering at scale. Interdisciplinary collaboration with research scientists and engineers, plus technical mentorship and project leadership depending on experience.
The Alan Turing Institute London £96,000 – £115,000 pa
Provides scientific and strategic leadership for the AI for Physical Systems research area, shaping a portfolio spanning probabilistic modelling, uncertainty quantification, efficient architectures and physics-informed machine learning. Leads a multidisciplinary team of researchers and engineers, taking methods from foundational research through to deployment on real-world resilience challenges, and builds collaborations across academia, government and industry.
