You will work with Python, AI coding agents (e.g., Claude Code, Codex), and potentially AWS, Kubernetes, and Temporal. Unitary builds Virtual Agents — AI-powered software workers that automate complex workflows end-to-end in customer tools, focusing on commercial insurance, marketplaces, and primary healthcare.

Lead the ML Infrastructure team using ArgoCD, Kargo, Jenkins, Vertex AI, SageMaker, Terraform, Helm, and GCP/AWS/Azure. Pavo AI builds Enterprise Superintelligence—systems that own business outcomes and improve through experience. This role drives DevOps, MLOps, and Agent Ops across R&D and production.

Build and deploy recommendation, bidding, attribution, and audience targeting models using Python, PyTorch or TensorFlow. You will define the ML and MLOps technical direction for a technology scale-up that powers personalised digital advertising and customer engagement.

Work with AWS, Kubernetes, Slurm, PyTorch, and distributed training frameworks to build foundation model AI for physical intelligence, enabling robots to learn tasks from a single human demonstration. This hands-on role focuses on GPU compute optimisation, cluster scheduling, and high-performance networking within a small, elite team in London.

You'll use Python, LLMs, generative AI, prompt engineering, agentic workflows, and computer vision as the founding AI engineer. The early-stage company builds agentic workflows and AI-powered video tools for large-scale media environments.

Spectrum, a JetBrains incubator project, builds an AI-powered knowledge platform that ingests code, docs, and issues to create a unified ontology for semantic search and reasoning. You'll design and own the ML/LLM stack—including prompt engineering, agentic frameworks (LangChain, LlamaIndex), RAG systems, and vector databases—while establishing MLOps practices and growing the ML team. The role requires 5+ years ML/AI experience with 2+ years in LLMs, strong Python skills, and hands-on expertise deploying production LLM applications.

You will work daily with AWS, Kubernetes, Slurm, PyTorch and distributed training frameworks to optimise GPU compute, cluster scheduling and high-performance networking. The team is building foundation-model AI for physical intelligence, creating a system that lets robots learn complex physical tasks from a single human demonstration.

PyTorch, Kubernetes, AWS, Python, and high-performance computing power large-scale distributed training infrastructure. The founding role builds systems for a robotics AI that learns complex tasks from a single demonstration, scaling across hundreds of GPUs while optimizing throughput and infrastructure costs.

Design AI solutions using Aion's enterprise platform, Large Language Models, Generative AI, RAG, AI agents, and cloud services (AWS, Azure, GCP) with Kubernetes, Docker, APIs, and microservices. You will partner with enterprise customers to architect and deliver production-ready AI applications, guiding them from concept to deployment on a unified platform.

Python, cloud infrastructure (GCP, AWS, Azure), and REST APIs are used to architect and build custom product demonstrations and proof-of-concept scripts for complex data ingestion, pipeline automation, and platform integrations. Encord’s platform indexes, curates, annotates, and evaluates data across the full AI lifecycle.

Autodesk★4.1 (589)· London

Use Python, CI/CD (GitHub Actions, Jenkins), and AI-powered testing tools to build and maintain automated test suites for AI-powered applications. This role ensures the quality, reliability, and performance of Autodesk’s AI features through API testing and validation of machine learning model outputs.

You’

Autodesk★4.1 (589)· London

You'll work with Python, CI/CD tools (GitHub Actions, Jenkins), and ML frameworks like PyTorch and TensorFlow to define quality standards for ML systems at Autodesk's Research Enablement team. You'll design model evaluation suites, test CAD RL outputs, automate QA workflows, and build quality gates for training and deployment pipelines. You'll report to an Engineering Manager and mentor teams on ML QA best practices across this globally distributed team.