Artificial Intelligence Jobs

PhysicsX · Singapore

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.

Wayve · London

Develops embedded software connecting an AI Driver to vehicle steering, braking and propulsion systems, working hands-on with benches and vehicles. Involves MATLAB/Simulink development, drive-by-wire actuation logic, CAN/CAN-FD diagnostics, OTA updates and ADAS/HMI interfaces, collaborating with OEMs, suppliers and controls engineers.

Wayve · London

Owns the autonomy performance measurement framework for a public-scale robotaxi deployment, defining metrics, dashboards and release gates for an end-to-end learned driving system with no existing playbook. Runs the performance improvement loop across model, robot software, backend and evaluation tooling, leading cross-functional execution without direct reporting authority while staying hands-on in Python, C++ and SQL.

Faculty · London

Build and scale AI-powered systems for public sector clients, owning the full stack from Python/FastAPI backends to TypeScript and React or Vue frontends. Work in cross-functional teams with data scientists and product managers, deploying to AWS, Azure or GCP with Docker and building data pipelines and cloud infrastructure. May require UK Security Clearance and occasional on-site work with government clients.

Faculty · London

Builds and deploys production-grade machine learning systems for national security and AI safety clients, including LLM applications, multi-agent tooling and evaluation of frontier models. Involves technical scoping, architectural decisions, and advising stakeholders, with reusable infrastructure work alongside engineers and data scientists.

Synthesia · Seattle, United States · US$200,000 pa

Builds and operates core enterprise platform systems, ensuring security, reliability and scalability for large customers, and contributes to a new API suite exposing Avatar technology to external creative tools. Owns multi-month, cross-team projects, translating business and regulatory requirements into scalable designs, and works with Python, React and AWS alongside product, security, legal and infrastructure partners.

OpenAI · London

Builds and operates the platform that deploys, configures and manages models across ChatGPT, spanning deployment orchestration, rollouts, operational automation and capacity management. Works with research, infrastructure and product teams to turn complex operational processes into reusable, scalable platform capabilities, with a focus on reliability, observability and developer experience.

Medtronic · London · £95,200 – £142,800 pa

Builds and maintains the internal platform tooling that other engineering teams rely on, spanning Infrastructure as Code with OpenTofu, EKS and Helm, CI/CD automation, and observability with OpenTelemetry. Works across software groups to remove developer productivity gaps and supports AI agentic workflows, with technical mentorship expected. Based in London with two to three days a week in the office.

Becrypt Ltd · London

Hands-on principal-level individual contributor leading AI adoption across engineering, product, IT and operations. Designs and builds agentic workflow platforms, AI gateways and Docker-based services using Python, FastAPI, TypeScript, MCP/FastMCP, OpenAI and AWS Bedrock, while owning AI policy, governance and risk frameworks. Also probes AI systems for vulnerabilities and advises leadership on AI strategy.

Scale AI · London

Builds and scales backend systems and APIs that bring large-scale generative AI products to production for enterprise customers. Work spans distributed data systems, model serving, deployment pipelines, and cloud infrastructure across AWS, Azure, or GCP, in close collaboration with ML and product teams. The role centres on reliability, latency, throughput and cost optimisation in hybrid and multi-cloud environments.

Emponics · Bristol · £80,000 – £130,000 pa

This role involves owning the data infrastructure for AI deployments, integrating AI solutions into business systems, and ensuring reliable and scalable production-grade pipelines. You will work closely with AI strategists to take prototypes to production, manage data models, and troubleshoot integration issues.

Synoptix · Bristol · £65,000 – £85,000 pa

Lead the technical vision and delivery of bespoke AI research programmes, spanning object detection, neural network resilience, AI assurance and synthetic data. Day-to-day work includes high-throughput vision systems on NVIDIA Jetson hardware, CUDA/TensorRT acceleration, multi-modal thermal and optical sensing, and real-time tracking, while guiding junior engineers and engaging senior stakeholders.

Synthesia · London

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.

WeDoTech · London · £120,000 – £160,000 pa

Hands-on technical leadership role building and scaling enterprise Generative AI, LLM and agentic AI systems across multiple engineering teams. Involves designing production-grade RAG and multi-agent solutions, shaping reusable AI frameworks and standards, and ensuring reliability and governance in a regulated financial services environment.

Helsing · Berlin, Germany

Integrates state-of-the-art AI and software into complex customer systems, working directly with avionics engineers and partners to understand unfamiliar data structures, APIs and network protocols, then building adapters to connect them to Helsing's cloud infrastructure. Owns delivery end-to-end, from formalising requirements and fixing bugs to shipping features and running technology demonstrations in simulations or on real systems.