Machine Learning Jobs

• New York, United States
• Hybrid
• US$150,000 – US$250,000 pa
• Senior

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 with Python/FastAPI microservices, and cloud deployment using Kubernetes, Terraform and AWS/Azure, with frequent on-site travel to customer sites.

FastAPI · Helm · Kubernetes +3

• New York, United States
• Hybrid
• US$150,000 – US$190,000 pa
• Mid-level

Builds and deploys machine learning systems for industrial physics challenges, designing reliable data pipelines and working with 3D point-cloud and mesh data. Partners directly with customers on-site to embed AI models into practical tools, translating R&D outputs into reusable libraries and products. Requires Python, cloud, and MLOps experience with frequent travel.

TensorFlow · MLflow · Spark +6

• London
• On-site
• Mid-level
• Permanent

Own the technical side of an in-house AI server for a boutique tax chambers, from configuring GPU hardware and serving LLMs to building RAG pipelines and MCP connectors that link models to solicitors' tools. The role also covers security, monitoring and backups, plus training colleagues and tracking new model releases.

MCP · Docker · Git +2

• Cranfield
• £21,805 pa
• Entry level
• Contract

A PhD studentship researching AI-on-RAN, optimising distributed and mobile inference of large language models by adapting token, memory and bandwidth use in real time. The work sits across AI and telecoms, with training alongside other PhD students in the EPSRC Future Telecommunications Hubs network and exposure to defence, government and infrastructure stakeholders.

AI-on-RAN · 6G

• London
• £23,805 pa
• Entry level
• Contract

Develop a framework combining a differentiable reacting flow solver with a machine learning-based controller to anticipate and mitigate extreme events such as flashback and thermoacoustic instabilities in hydrogen and SAF combustion. The role sits within the European CONTEXT project at the intersection of scientific machine learning and fluid dynamics, using high-fidelity simulations and Imperial's HPC facilities.

HPC

• Sunbury-on-Thames
• Hybrid
• Senior

Designs, builds and operates scalable cloud data platforms, ELT/ETL pipelines and analytical datasets supporting Finance, with Azure and Databricks/Fabric/Snowflake preferred. Combines strong software engineering (CI/CD, testing, automation) with data modelling, governance and technical leadership, mentoring engineers and working with product, architecture and Finance stakeholders.

Azure · Databricks · Microsoft Fabric +5

• New York, United States
• Hybrid
• US$200,000 – US$250,000 pa
• Senior

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.

Python · PyTorch · Pandas +5

• New York, United States
• Hybrid · 2–3 days in office
• US$120,000 – US$240,000 pa

Builds and deploys deep learning and probabilistic models to predict and control physical systems, working alongside simulation engineers and customers. Owns data pipelines and production-ready models, and travels to customer sites roughly 3-4 weeks per quarter to co-develop solutions on-site.

NumPy · SciPy · Pandas +2

• New York, United States
• US$150,000 – US$250,000 pa

Builds and deploys customer-facing applications that put physics AI models into production, working directly with customers, data scientists and ML engineers. Involves rapid prototyping, RESTful APIs in Python (FastAPI), microservices, and cloud deployment with Kubernetes, Helm, Terraform and AWS/Azure, plus quarterly on-site travel.

FastAPI · Helm · Kubernetes +3

• New York, United States
• Hybrid
• Lead

Lead the ML approach to manufacturing and MRO data, taking models from design through deployment in customer production environments across cloud, on-prem and edge. Work with sparse, drifting sensor and process data, fusing physics simulation with shop-floor measurement, and set uncertainty quantification and validation standards. Requires travel to customer sites 3-4 weeks per quarter and mentoring of engineers and data scientists.

PyTorch · Pandas · FastAPI +2

• San Francisco, United States
• Hybrid
• US$150,000 – US$190,000 pa

Delivery-focused role embedding physics-aware AI models into customer tools, building reliable ML data pipelines and working with 3D point-cloud and mesh data. Spans data science and software engineering, translating R&D into reusable libraries and products, with regular on-site customer travel.

TensorFlow · MLflow · Spark +6