Machine Learning Jobs

• Singapore

Builds and deploys customer-facing applications that put physics AI models into production, working directly with customers, data scientists and ML engineers. Involves full-stack work with Python, FastAPI REST APIs and microservices, plus cloud deployment using Kubernetes, Helm, Terraform and AWS/Azure. Expects travel to customer sites in Asia, North America and Europe for 3-4 weeks per quarter.

FastAPI · Helm · Kubernetes +3

• Singapore
• Lead

Builds and delivers high-fidelity multi-physics CFD models, from geometry clean-up and meshing to simulation and post-processing, integrating experimental data for validation. Automates parametric CAD and simulation pipelines for design optimisation and DoE, generating datasets for machine learning surrogates. Works on-site with customers and mentors junior colleagues, with global travel up to 2-3 weeks per quarter.

Star-CCM+ · OpenFOAM · Fluent +4

• London
• Hybrid
• Senior
• Permanent

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.

PyTorch · TensorRT · GitHub Actions +1

• London
• Hybrid
• Mid-level
• Permanent

Develops embedded software connecting an AI Driver to vehicle steering, braking and propulsion systems, working with MATLAB/Simulink, CAN/CAN-FD tooling and vehicle ECUs. Involves bench and in-vehicle testing, drive-by-wire bring-up, OTA updates and ADAS/HMI interfaces alongside OEMs and suppliers.

MATLAB · Simulink · CANoe +4

• Birmingham
• Remote
• £90,000 – £110,000 pa
• Lead

Hands-on senior role designing and building scalable data platforms, ETL/ELT pipelines and Data-as-a-Service capabilities that power analytics, machine learning and AI products for enterprise clients. Involves setting standards for data quality, governance and observability, plus secure data services and APIs, with scope to build and lead a wider data function.

AWS

• Cambridge
• Remote
• £85,000 – £100,000 pa
• Lead

Hands-on senior role designing and building scalable data platforms, ETL/ELT pipelines and Data-as-a-Service capabilities that power analytics, machine learning and AI products for enterprise clients. Involves setting standards for data quality, governance, lineage and observability, plus secure data services and APIs, with scope to build and lead a wider data function.

AWS

• United Kingdom
• Hybrid
• Lead

Senior technical role architecting agentic AI systems for defence and security clients, acting as technical lead on complex projects and setting departmental technical direction. Involves hands-on engineering in Python, client-facing proposal work, mentoring engineers and conducting technical interviews. Requires SC security clearance and UK residency.

• London
• Permanent

Open speculative application for a TechBio company, inviting CVs from computational biologists, life sciences researchers, and ML engineers with high-performance multi-GPU computing experience. Successful candidates would join matrixed, interdisciplinary teams combining biology, computation and engineering in wet and dry labs to advance drug discovery.

• London
• Senior
• Permanent

Processes and QC's paired single-cell RNA-seq and scATAC-seq at scale, building Nextflow pipelines and multimodal training datasets in Zarr for GPU-accelerated model training. Partners with wet lab scientists and ML modellers, contributing to evaluation frameworks that test whether models capture meaningful biology.

STARsolo · Alevin-fry · Chromap +7

• London
• On-site
• Junior
• Internship

The Junior Quant Researcher will conduct quantitative research using statistical and predictive modeling techniques, manage the full lifecycle of strategy development, and create computer-based models to predict financial market movements. The role involves working closely with a start-up environment, requiring a strong background in machine learning and programming.

• London
• Senior

Owns end-to-end delivery of AI initiatives, starting with a Class II medical device system combining an AI layer with deterministic components. Involves NLP, LLMs, knowledge graphs, evaluation pipelines and clinician review workflows, working closely with clinical, quality and product stakeholders in a regulated environment.

AWS · GCP · Azure +7

• London
• Senior

Own the end-to-end technical delivery of AI initiatives, starting with the Clinical Brain, a Class II medical device combining an AI layer with deterministic components. Work with clinical, quality and data teams on NLP, LLMs, knowledge graphs, evaluation pipelines and LLMOps, building to regulated safety and traceability standards.

Neo4j · Cypher · GraphRAG +6

• 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

• Manchester
• Remote
• £30,000 – £45,000 pa
• Junior

A 15-step online, self-paced AI traineeship covering Python, NumPy, Pandas, scikit-learn, Hugging Face, prompt engineering, RAG and vector databases, culminating in an AWS Certified Cloud Practitioner exam. It is a paid training course with fees, offering 1-to-1 tutor and recruitment support plus a job guarantee, rather than a salaried position.

Jupyter Notebook · VS Code · Streamlit +7

• London

Develop and test alpha-generating strategies for systematic equity portfolios using statistical techniques, machine learning, and large-scale data analysis. Work closely with portfolio managers to transition research into live trading, building predictive models and robust research infrastructure in Python. Focus on rigorous backtesting, signal validation, and collaborative research in a high-impact environment.