Machine Learning Jobs

PhysicsX · New York, United States · US$200,000 – US$250,000 pa

Owns deployment of ML models and engineering surrogates (deep learning on CAE/CFD/FEA data, time-series forecasting, anomaly detection, optimisation) into customer production environments, building scalable training and inference pipelines and APIs on cloud and on-prem. Works directly with customers on-site, leads scoping and architecture, and mentors engineers and data scientists. Tech stack includes Python, PyTorch, Pandas, fastAPI, Scipy and Kubeflow.

PhysicsX · London

Owns deployment of ML models and engineering surrogates (deep learning on CAE/CFD/FEA data, time-series forecasting, anomaly detection, optimisation) into customer production environments, using Python, PyTorch, Pandas, fastAPI, Scipy and Kubeflow across cloud and on-prem. Leads scoping and architecture for data/ML systems, mentors engineers and data scientists, and travels to customer sites roughly 3-4 weeks per quarter.

Faculty · London

Deliver bespoke, production-grade machine learning systems for government and national security clients, spanning the full ML lifecycle from scoping to deployment. Work with Python, Scikit-learn, TensorFlow or PyTorch, plus cloud platforms and Docker/Kubernetes, while advising stakeholders and setting standards for deploying ML at scale.

Faculty · London

Builds and deploys production-grade machine learning systems for national security and AI safety clients, including LLM applications, multi-agent tooling and evaluation of frontier models. Involves leading technical scoping and architecture decisions, defining deployment standards, and advising customers, working alongside engineers, data scientists and commercial leads.

WeDoTech · London · £120,000 – £160,000 pa

Hands-on technical leadership role building and scaling enterprise Generative AI, LLM and agentic AI systems across multiple engineering teams. Involves designing production-grade RAG and multi-agent solutions, shaping reusable AI frameworks and standards, and ensuring reliability and governance in a regulated financial services environment.

Wayve · London

Owns the engineering release process for autonomous driving models, defining branching strategies, release cadence, configuration management and quality gates. Reviews model, architecture and code changes against intended outcomes, assesses evaluation results for safety and performance, and builds CI/CD tooling with PyTorch, TensorRT and GitHub Actions. Works across ML engineering, AI Platform, evaluation and product to unblock delivery bottlenecks.

Capital One · Nottingham

Lead the development of proprietary deep learning models to enhance underwriting capabilities, building neural networks such as LSTMs, RNNs and Transformers on structured and unstructured data. Set technical direction, mentor junior team members and drive prototypes through to production in close collaboration with business stakeholders.

Capital One · Mansfield

Lead the development of proprietary deep learning models to enhance underwriting capabilities, building neural networks such as LSTMs, RNNs and Transformers on structured and unstructured data. Set technical direction, manage projects, communicate with stakeholders and mentor junior team members, taking ideas from prototype to production in a hybrid Nottingham office.

NVIDIA · Switzerland

Develops performance optimizations in deep learning frameworks using JAX, contributing to JAX core components and driving peak performance on NVIDIA hardware. Works with AI researchers to build future-proof models and tooling that bridges numerical computing, simulation and deep learning research. Requires C/C++ and Python skills plus deep knowledge of ML framework internals and CPU/GPU architectures.

NVIDIA · United Kingdom

Develops performance optimizations in deep learning frameworks using JAX, contributing to JAX core components and driving peak performance on NVIDIA hardware. Works with AI researchers to build future-proof models and tooling that bridges numerical computing, simulation and deep learning research. Requires C/C++, Python and machine learning framework internals expertise.

Latent Labs · London

Architect novel generative models to design functional proteins, working in an interdisciplinary team of ML researchers, protein engineers and biologists. The role spans data curation, evaluation metrics, rapid prototyping, cloud training and inference optimisation, plus planning wet lab testing campaigns with the bio team.

Copello · Bristol · £54,000 – £58,000 pa

Develops and evaluates image processing, computer vision and deep learning algorithms for imaging sensor systems, covering segmentation, noise removal, tracking, pose estimation and classification. Works on-site in Bristol within a cross-functional team, supporting research into emerging AI/ML technologies and integrating algorithms into larger operational systems.

Randstad Technologies Recruitment · London · £432 – £484 pd

Design and deploy production-grade Generative AI workflows and backend security guardrails for a global financial institution's AI Centre of Excellence. The role spans PII/MNPI sanitisation, prompt-injection defence, Python or Java GenAI and ML solutions, AWS/Azure data pipelines with Airflow, plus roughly 20-25% frontend and UI work for guardrail tooling, alongside production support and evaluation metrics.

Isomorphic Labs · London

Join the model performance and scaling team to implement and optimize LLM post-training methods on frontier models, working with research teams to turn new methods into production systems. The role focuses on distributed training and inference performance, low-precision methods, and frameworks like JAX or PyTorch, in a hybrid London setting.

Cirrus Selection · Filton · £45,000 – £58,000 pa

Research, develop and evaluate image processing and computer vision algorithms within a specialist team, applying AI and deep learning to segmentation, object detection, tracking and classification. Work spans optical flow, pose estimation, particle filtering and computational imaging using MATLAB, Simulink and Python with PyTorch, collaborating with multidisciplinary engineers to turn research into practical applications.