382 jobs for “machine learning scientist”
CrowdStrike · 1001–5000 people · Public
Leads a team of three data scientists building machine learning and agentic AI systems for threat detection, malware analysis and security automation. Sets technical standards and strategy for ML development, deployment and monitoring, while staying hands-on with Python, big data and cloud infrastructure.
AWS · Linux
Isomorphic Labs · 201–500 people · Series A
Lead computational chemistry strategy across drug discovery programs, applying AI-first approaches to hit finding, molecular design and lead optimisation. Work with ML scientists to embed generative models into cheminformatics workflows, with some roles managing 2-3 computational chemists.
REINVENT · AlphaFold · Large Language Models
Isomorphic Labs · 201–500 people · Series A
Technically lead the Platform group, designing, operating and refining cloud infrastructure across research, development and production. Work spans GCP, Kubernetes, Docker, Terraform and Kapitan, plus CI/CD, observability and tooling, partnering with scientists, ML researchers and product teams. Reports to the Director of Engineering in a small, fast-moving team.
GCP · Kubernetes · Docker +7
PhysicsX
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.
FastAPI · Helm · Kubernetes +3
PhysicsX
Builds the simulation side of Large Physics Models, generating multi-fidelity CFD data to train large-scale ML models. Develops solvers and numerical methods in differentiable or GPU-native frameworks such as JAX or NVIDIA Warp, using commercial and open-source CFD tools for reference data. Works in a research team alongside ML researchers, shaping data generation strategy and fidelity tradeoffs.
JAX · NVIDIA Warp · OpenFOAM +2
Faculty
Design, build and deploy secure, scalable cloud infrastructure for AI and ML workflows, working in a cross-functional team with data scientists and ML engineers. The role involves infrastructure-as-code, containerised deployments and client-facing requirements gathering, with a focus on Azure, AWS, Terraform, Docker and Kubernetes.
Azure · AWS · Terraform +3
CrowdStrike · 1001–5000 people · Public
Lead a team of data scientists working at the intersection of machine learning, large language models and cybersecurity, translating business needs into technical objectives and driving research into production ML systems. The role combines people leadership, roadmap planning and stakeholder communication with hands-on work on malware detection and threat analysis at massive scale.
AWS · Linux
Faculty
Builds client-facing products that visualise insights from data scientists, working in cross-functional delivery teams within the National Security and AI Safety unit. Owns frontend standards and reusable component libraries across the company, sets up deployment infrastructure, and mentors other engineers. Requires React, TypeScript, ES6, HTML/CSS3 and data visualisation such as d3, with cloud exposure to AWS, GCP or Azure.
React · D3 · AWS +3
Medtronic · 10000+ people · Public
Builds and maintains a Python-based SaaS platform for project, data and annotation management used by internal machine learning engineers and data scientists. Works with data and platform engineers to handle growing data volumes, and collaborates with stakeholders to define requirements and validate features in an iterative, quality-driven environment.
AWS · GCP · Docker +4
Medtronic · 10000+ people · Public
Designs, builds and maintains AWS and on-prem infrastructure used by ML engineers, data scientists and product teams, with a strong focus on Kubernetes, Helm, Terraform/OpenTofu and GitOps delivery. Also handles platform service requests, incident diagnosis and reliability improvements, working across engineering teams in a surgical AI centre of excellence.
Kubernetes · Helm · AWS +4
Wayve · 11–50 people · Series A
Lead a team of ML engineers and applied scientists building synthetic data from generative world models like GAIA-3, turning them into a production engine for training-grade driving experience. Own the loop end to end, from checkpoint and large-scale GPU inference to artefacts landing in driving-model training, while setting technical direction on rig transfer, pose transfer and controllability.
PyTorch · GAIA · GPU +1
Harnham - Data and Analytics Recruitment
Build and own core platform capabilities including application infrastructure, APIs and shared services, developing full stack applications in Python, React and TypeScript. Surface machine learning and predictive analytics insights through intuitive interfaces and integrate AI/LLM workflows into production, working in a small innovation team with significant architectural ownership.
Python · React · TypeScript +1
Wayve · 11–50 people · Series A
Owns shared onboard runtime foundations for autonomous vehicles: application frameworks, communication middleware, lifecycle management, health monitoring and observability. Day to day involves C++ development, end-to-end latency and performance profiling across OS scheduling, memory hierarchy and thread synchronisation, plus porting the stack to new operating systems and compute platforms.
NVIDIA Nsight · Lumos · tegrastat +5
The University of Manchester
Develops and applies machine-learning and AI methods to protein engineering and enzyme design, analysing large biological, chemical and experimental datasets and building predictive models and computational tools. Works within an interdisciplinary group spanning the Manchester Institute of Biotechnology and Department of Chemistry, providing ML and data-science expertise, training and support to postdocs and students, and contributing to publications and funding bids.
University of Exeter
Develops a multiscale Scientific Machine Learning digital-twin framework to predict thermal trajectories, torque capability and efficiency in low-speed, high-torque electric propulsion under extreme duty cycles. Combines physics-informed hybrid architectures (DNNs, neural state-space models, Neural ODEs) with safety-constrained dynamometer experiments at the Centre for Future Clean Mobility, in collaboration with marine power-systems company Ecomar Innovations.
Neural Ordinary Differential Equations · Deep Neural Networks · Recurrent Neural Networks +2
