Artificial Intelligence Jobs

• 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 3-4 weeks of travel per quarter to customer sites.

FastAPI · Helm · Kubernetes +3

• London
• Hybrid · 3 days in office
• Senior

Owns deployment of ML models and engineering surrogates (deep learning on CAE/CFD/FEA data, forecasting, anomaly detection) into customer production environments, building scalable training and inference pipelines with Python, PyTorch, Kubeflow and fastAPI. Leads scoping and architecture, mentors engineers, and travels to customer sites roughly 3-4 weeks per quarter.

Python · PyTorch · Pandas +6

• London
• Senior

Builds full-stack AI applications directly alongside public sector clients, scoping requirements, gathering feedback and shipping production-grade systems. Work spans React and TypeScript front ends, Python/Node backends, and cloud infrastructure on AWS, Azure or GCP, with a focus on reusable platform features and deploying into customer environments.

React · TypeScript · Next.js +7

• London
• Senior

Forward-deployed full-stack engineer who partners directly with public sector clients to turn ambiguous mission requirements into production-grade AI applications. Leads architecture across React, Node.js, Python, Kubernetes and global cloud infrastructure, mentoring staff and shaping reusable platform patterns. Founding-member role on Scale's Global Public Sector team.

Python · Node.js · React +4

• 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

• London
• Hybrid · 3 days in office
• Senior

Join a newly formed LLM Engineering team to architect and own systems bringing large language models into scientific and business processes. Work spans LLM serving stacks, agentic infrastructure, RAG, evaluation frameworks, and fine-tuning, partnering with researchers to translate scientific needs into technical AI specifications.

vLLM · GCP · MCP

• London
• Hybrid · 3 days in office
• Mid-level

Joins the Compute Infrastructure team to keep a planet-scale GPU accelerator fleet healthy and efficient. Builds observability systems, telemetry pipelines and automated recovery mechanisms, hunting compute waste and optimising accelerator utilisation and workload goodput alongside ML platform and research teams.

GCP · Kubernetes · Nvidia GPU +1

• London
• Hybrid
• Mid-level
• Permanent

Deploys, integrates and optimises AI-driven security technology across network, cloud, OT and email environments for post-sales customers. Handles deployment projects against SLOs, resolves support tickets and acts as a trusted advisor on security infrastructure design, liaising with sales, customer success and operations.

AWS · Microsoft · Cisco +2

• Cambridge
• Hybrid
• Senior
• Permanent

Acts as the go-to authority in a chosen reliability specialism—observability, performance engineering, data infrastructure, security-focused SRE or network reliability—defining standards and best practices across SRE, Platform Engineering and DevSecOps. Builds tooling and automation, leads deep-dive investigations into systemic issues, and contributes to incident response and on-call rotations in a cloud-native, Kubernetes-based environment.

AWS · GCP · Azure +1

• London
• Remote
• Lead
• Permanent

Hands-on principal IC role owning the ML platform that lets researchers and product teams train, serve and deploy generative models. Work spans research infrastructure, production serving, internal tooling and agent-oriented automation, with a focus on reliability, scalability and GPU/cloud resource efficiency. Distinct for its systems-generalist framing rather than pure ML engineering.

Kubernetes · Terraform · Datadog +4

• United Kingdom
• Remote
• Senior

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, ML framework internals, and CPU/GPU architecture expertise.

JAX · PyTorch · TensorFlow +4

• London
• Hybrid
• Mid-level
• Permanent

Design, build and operate core components of the Auto-review and Guardian platform, evaluating agent actions in real time to detect harmful behaviour and support safe recovery or escalation. Work spans architecture, implementation, evaluation and operations, partnering closely with alignment researchers, product, security and infrastructure teams to make agent safety a foundational capability.

OpenAI

• 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

• 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