Artificial Intelligence Jobs

• United Kingdom
• Remote
• Permanent

Owns the path from research checkpoint to production serving infrastructure, optimising inference latency, throughput and cost using quantization, distillation, KV-cache optimisation, batching and custom kernels. Builds high-performance serving systems for real-time streaming workloads and tooling that lets researchers ship models quickly and safely.

vLLM · SGLang · CUDA +2

• United Kingdom
• Remote
• Mid-level
• Permanent

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, web-scale deduplication, recrawl and freshness strategies, politeness and rate limiting, plus tooling for researchers to request and explore crawled data.

Kubernetes

• New York, United States
• Hybrid
• US$200,000 – US$300,000 pa
• Lead

Partners with engineering teams to embed security into CI/CD pipelines, lead threat modeling and secure design reviews for web apps, APIs and cloud services, and manage the product vulnerability lifecycle. Also drives secure coding standards, delivers training, and engages with customers during security reviews, with a strong DevSecOps and application security focus.

GitHub · GitLab · Semgrep +4

• 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, optimization) into customer production environments, building scalable training and inference pipelines and APIs on cloud and on-prem. Leads scoping and architecture design, mentors engineers and data scientists, and travels to customer sites roughly 3-4 weeks per quarter to embed AI models on-site.

Python · PyTorch · Pandas +3

• London
• Hybrid
• Mid-level
• Permanent

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

• London
• Hybrid
• Mid-level
• Permanent

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

• London
• Hybrid
• Permanent

This is a fantastic opportunity to join Luminance, the pioneer of Legal-Grade™ AI for enterprise. Backed by internationally renowned VCs and named in both the Forbes AI 50 list of ‘Most Promising Private AI Companies in the World’ and Inc.…

• Cambridge
• Hybrid
• Permanent

Designs and develops web-based applications that apply core AI technology to legal-sector use cases, working across UI, backend, or a mix of both. Uses a modern stack including JavaScript frameworks like React or Vue and server-side technologies such as Node.js, PostgreSQL, Elasticsearch or Redis. Open to graduates as well as more senior engineers with proven experience.

Backbone.js · React · Vue.js +6

• Singapore

Build and deploy customer-facing applications powered by physics AI models, working directly with customers, data scientists and ML engineers. Involves rapid prototyping, Python/FastAPI microservices, and cloud deployment with Kubernetes, Terraform and AWS/Azure, plus quarterly travel to customer sites.

FastAPI · Helm · Kubernetes +3

• Singapore
• Lead

Deliver high-fidelity multi-physics CFD simulations, from geometry clean-up and meshing to post-processing and validation against experimental data. Build parametric CAD models and automate simulation pipelines for design optimisation and DoE, generating datasets for machine learning surrogates. Partner directly with customers on-site and mentor junior colleagues.

Star-CCM+ · OpenFOAM · Fluent +4

• Singapore
• Lead · manages people

Lead technical delivery of physics-informed AI, architecting deep learning models and optimisation methods to predict and control physical systems. Own data pipelines and pre-processing best practices while mentoring a team and collaborating with simulation engineers and customers. Involves frequent on-site customer travel across multiple regions.

NumPy · SciPy · Pandas +2

• Singapore
• Lead

Lead the deployment of ML models and engineering surrogates—deep learning on CAE/CFD/FEA data, time-series forecasting, anomaly detection, and optimisation—into customer production environments. Own scoping, architecture, and success metrics for data/ML systems built with Python, PyTorch, Pandas, fastAPI, Scipy, and Kubeflow across cloud and on-prem. Mentor engineers and data scientists while travelling 3-4 weeks per quarter to customer sites worldwide.

Python · PyTorch · Pandas +3

• Singapore
• Senior

Customer-facing engineering role building and deploying applications that put physics AI models into production for industrial clients. You will work directly with customers, data scientists and ML engineers, prototyping tools, iterating on user feedback, and building Python FastAPI microservices deployed on Kubernetes, Terraform and AWS/Azure.

Python · FastAPI · Helm +4

• Cambridge
• Hybrid
• Mid-level
• Permanent

Joins a new Quality team to build and maintain a state-of-the-art QA process, writing end-to-end tests with Cucumber, Cypress and Playwright and investigating defects to prevent recurrence. Acts as the link between Development and Product, formalising specifications into testable units and driving the shift toward automation as standard, including release quality metrics.

Cucumber · Cypress · Playwright

• Cambridge
• Hybrid
• Mid-level
• Permanent

Owns the software development lifecycle that produces packaged releases of an enterprise legal AI platform for distribution to clients. Day to day involves keeping the lifecycle efficient, unblocking the tech team, and analysing the SDLC for weak points to build human or automation solutions improving efficiency, reliability and visibility.