Machine Learning Jobs in York

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

Builds and validates multiphase CFD and coupled CFD-DEM models, covering geometry clean-up, meshing, simulation, post-processing and experimental validation for granular and particle-laden systems. Automates parametric CAD and simulation pipelines for design optimisation and DoE, generating datasets for ML surrogate models on cloud and HPC resources, while working on-site with customers and mentoring junior colleagues.

Cohere · London

Own end-to-end technical program delivery for UK public sector and defence customers, from initial deployment through production scaling, acting as the bridge between frontier AI capabilities and government requirements. Navigate procurement, security authorisations, data sovereignty and compliance while coordinating Applied ML, Forward Deployed Engineering, Platform and Product teams. Work spans air-gapped, on-premises and highly regulated environments.

Cohere · London

Designs next-generation evaluation benchmarks and infrastructure to measure LLM progress, including training LLM judges, refining data synthesis pipelines, and improving evaluation efficiency. Works cross-functionally to turn model feedback into trustworthy, repeatable evaluations, and builds scalable tools for probing model performance.

ElevenLabs · United Kingdom

Owns large-scale distributed web crawling systems that discover, fetch and extract data from billions of pages to feed frontier AI models. Work spans content extraction from messy HTML, deduplication at web scale, recrawl and freshness strategies, politeness and rate limiting, plus tooling for researchers to request and explore crawled data.

PhysicsX · Singapore

Customer-facing role owning the full customer journey, from onboarding and change management to roadmap planning and contract preparation. Works alongside simulation, software, ML engineers and data scientists to translate industry challenges into product opportunities, tracking engagement and measuring value. Based in Singapore, bridging advanced AI capabilities and practical engineering applications.

PhysicsX · New York, United States

Builds and runs high-fidelity CFD models from geometry clean-up and meshing through simulation and post-processing, using Star-CCM+, OpenFOAM or Fluent plus parametric CAD and Python/Java automation. Works with data scientists and ML engineers to generate simulation datasets for deep learning surrogates, running jobs on the Flux cloud platform and on-premise HPC, and supports customer engagements with occasional travel.

PhysicsX · Singapore

Build physical foundation models for industrial engineering, scaling training across multi-GPU cloud compute and turning research prototypes into robust, optimised implementations. Work at the intersection of data science and software engineering, owning research work-streams and translating results into reusable libraries, tooling and products alongside scientists, customers and partners.

PhysicsX · Singapore

Builds and operates a SLURM-on-Kubernetes HPC platform running 304 B200 GPUs for large-scale distributed training and CAE data generation. Splits time between platform engineering — hardening, storage, networking, observability — and hands-on debugging of job failures and throughput bottlenecks alongside researchers.

PhysicsX · London

Owns PX's editorial calendar and writes blog posts, web pages, thought leadership, scripts and event materials, translating deep technical work into clear narratives. Leads LinkedIn and Instagram, supports executive and technical speaking engagements, and partners with Talent on employer brand storytelling. Works cross-functionally with engineers, designers and external agencies in a fast-moving, high-ownership comms team.

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.

PhysicsX · Singapore

Build and deploy customer-facing applications that put physics AI models into production, working directly with customers, data scientists and ML engineers. The role spans rapid prototyping, backend development of Python/FastAPI REST APIs and microservices, and cloud deployment using Kubernetes, Helm, Terraform and AWS/Azure. It suits someone who can grasp complex engineering problems across industries and own solutions end-to-end.

Cohere · London

Builds and optimizes the synthetic data pipeline behind Cohere's frontier language models, running scalable inference on large GPU clusters and transforming web and code data with generative models. Involves data ablations, mixture experiments and model evaluation to improve token efficiency and training throughput, working closely with researchers and engineers.

Cohere · London

Interns work across frontend, backend, full-stack or infrastructure teams, shipping user-facing product code, building API platform features, designing data pipelines and scalable ML infrastructure, and contributing to CI/CD tooling and technical documentation. Interns are treated much like full-time engineers, with autonomy and ownership over production code, supported by mentors and team leads.

Cohere · London

Research internship focused on building and training large language models, with projects spanning base model training, retrieval augmented generation, evaluation, safety, and finetuning. Interns design and implement novel research ideas, ship state-of-the-art models to production, and publish results through papers, datasets, and code.