Deliveroo★3.3 (1.5k)· London

Build production infrastructure for Generative AI at Deliveroo and DoorDash, leading design and architecture for real-time GPU serving, high-throughput batch inference, and fine-tuning (SFT/DPO/LoRA) of open-weight LLMs and VLMs. Own the LLM Gateway, Agent Gateway, evals infrastructure, guardrails, and cost attribution, while mentoring engineers and setting technical direction for a small, high-leverage centralized GenAI platform team.

You'll work with Python, PyTorch, SGLang, large language models, embeddings/reranking models, PEFT/LoRA, RAG, prompt engineering, Git, Docker, microservices, and cloud infrastructure. The early-stage company builds AI-powered products transforming specialised financial markets, combining machine learning with real-world trading workflows. You'll collaborate with a small engineering team, founders, brokers, and traders on commercially impactful systems.

Working with Python, C++, and deep learning frameworks such as TensorFlow, PyTorch, or JAX, the role covers LLMs, deep learning optimization, reinforcement learning, and generative models. The research team applies AI to semiconductor design and optimization, alongside fundamental research supporting internal applications and the broader scientific community.

You’ll implement ML research ideas using Python or C++, alongside deep learning frameworks like TensorFlow, PyTorch or JAX. The team applies AI (including LLMs, reinforcement learning and generative models) to semiconductor design and fundamental research.

You'll design, build, and maintain scalable AI data pipelines, cloud-native backend systems, and distributed services using Python, REST APIs, and microservices with FastAPI, Flask, or Django. You'll productionise Machine Learning, NLP, and Generative AI models, work with SQL databases, AWS/Azure, Docker, and Kubernetes, and mentor juniors. The company specialises in AI and data engineering, transforming AI research into production systems.

You'll work with Python, FastAPI, TypeScript, React, and GCP, building services, data models, and APIs for a behavioral simulation platform that creates digital twins of populations. The team of 17 owns complex systems from design to deployment, orchestrating foundation models and data pipelines for major organizations.

Build LLM-powered agentic systems with persistent memory and tool orchestration, integrating Google Ads, Meta, and CRMs. Iterate on RAG pipelines, retrieval strategies, and domain-specific fine-tuning in Python. As the second ML hire, you deploy production-grade autonomous agents automating marketing operations for a Series A startup.

Legora★0.0· London

You will use Python, data analysis, surveys, interview protocols, and dashboards to study how lawyers and legal teams build proficiency with AI tools. The AI Transformation & Applied Research team turns this evidence into actionable recommendations for product, customer enablement, and go-to-market at Legora, an AI-native legal workspace.

RBC★3.9 (5.5k)· London

You will design, build, and maintain REST and GraphQL APIs and MCP servers using Python (FastAPI, Flask) serving ML models from Databricks for internal apps, CRM platforms, AI agents, and LLM applications. You will build middleware, prototype applications, and mentor junior colleagues in a team reporting to the Director, Data Science.

Google★4.3 (6.2k)· London

You will work with C++, Java, Kotlin, TypeScript, front-end frameworks, API development, machine learning, agentic coding, and GCP. This role is in Google Labs Science, which partners with DeepMind to transform GenAI research (e.g., Co-Scientist, AlphaEvolve) into products that accelerate scientific discovery.

You'll apply SRE discipline to AI platforms, building and operating Kubernetes infrastructure with Terraform, managing CI/CD pipelines (GitHub Actions, GitLab CI, Jenkins, Argo CD), using Python, and working with observability stacks like Datadog and Prometheus. You'll define SLOs, handle on-call and incidents, and partner with teams like the Inference Control Plane and Evals/Observability. The role is with CreateFuture, an AI-native consulting partner serving clients such as PayPal and adidas.

You'll work with Python, AWS, Kubernetes, Terraform, Docker, and Inspect, building tools and infrastructure for AI safety evaluations, model hosting, and research support. The team works on research-oriented software used across the AI Security Institute's work on frontier AI risks.

You will build and improve production detection models using NLP (transformers, embeddings, PyTorch, Hugging Face, scikit-learn), curating datasets and tuning classifiers for real-time sensitive-data classification. You will join Harmonic Security's Product Delivery team, which builds the AI Governance and Control (AIGC) platform that gives enterprises visibility and control over AI tool use to minimize risk.

You’ll build and operate Kubernetes infrastructure for AI workloads using Terraform, Python, and AWS/GCP, plus CI/CD pipelines (GitHub Actions, GitLab CI, Jenkins, Argo CD) and observability stacks (Datadog, Prometheus, Grafana). The role applies SRE discipline—SLOs, incident response, on-call—to AI platforms at CreateFuture, an AI-native consulting firm building digital products for clients.

This role involves leading AI/ML delivery using Python, LLMs, and techniques like prompt engineering, RAG, and agentic AI, with experience in frameworks such as PyTorch or TensorFlow. The team, a 150-strong AI and Data Practice, delivers production-grade AI solutions for clients in government, healthcare, defence, and commercial sectors. The manager will coach and develop a multidisciplinary team.

You will work with Python and modern ML tooling, owning the end-to-end AI/ML stack including model selection, fine-tuning, and inference infrastructure. The team builds specialized AI agents and inference infrastructure to apply LLM research to social science and public policy simulations for the public sector.

Google★4.3 (6.2k)· London

Work with C++, Java, Kotlin, TypeScript, front-end frameworks, APIs, and GCP to design and build machine learning and agentic systems. As part of Google Labs Science, you will partner with Google DeepMind to transform GenAI research into products that accelerate scientific discovery.

Work with Python, SQL, JavaScript/TypeScript, NumPy, Pandas/Polars, visualization libraries, and deep learning frameworks (PyTorch, JAX, or TensorFlow) to design and deploy full-stack data-driven applications and machine learning solutions. You will be embedded with Arsenal's Men’s First Team Analysis department, building tools that directly support coaching and analysis workflows.

You will work with LLMs, chain-of-thought prompting, RAG architectures, multi-agent tool use, Python, and FastAPI to build cognitive systems. The Science team designs memory and decision-making frameworks to create synthetic populations for behavioral prediction.

Build and ship AI features using Python, PyTorch/JAX, LLMs (OpenAI-style APIs, LLaMA, Qwen), vLLM, and vector DBs. You'll work on a proactive smart assistant that handles long-running workflows, persistent context, multi-step reasoning, and external tool interaction for everyday users.

You'll work with Kubernetes, Terraform, Python, AWS or GCP, and CI/CD tools (GitHub Actions, GitLab CI, Argo CD), plus observability stacks (Datadog, Prometheus, Grafana). You'll bring SRE discipline to an AI platform hosting model serving, agent orchestration, and batch inference.

Optimize ML inference for edge accelerators and GPUs using TensorRT, CUDA, Qualcomm QNN, Triton, MLIR, and ONNX. The team deploys large transformer-based models on low-cost, low-power edge devices for Wayve's first driving product. You'll work across compilers, runtimes, and kernels with Python and C++.

Legora★0.0· London

You will design and run mixed-methods studies, build surveys, interview protocols, and quantitative measures, and analyze usage and outcome data using Python and standard research tooling. The AI Transformation & Applied Research function studies how legal teams adopt and build skill with AI, turning evidence into recommendations for Legora’s product, customer enablement, and go-to-market motion. This is a hands-on, mid-level role.

Build REST and GraphQL APIs and MCP servers in Python (FastAPI, Flask) to expose ML models from Databricks to internal apps, CRM platforms, and AI agents at RBC. Work within the Data Science team to architect the serving layer, handling contract design, authentication, and documentation.

Design and deploy enterprise-grade AI applications using Python, LLMs (OpenAI, Claude, Gemini), RAG, vector databases, and LangChain/LangGraph on Azure/AWS with Docker and Kubernetes. Ascendion is an AI-first software engineering services provider delivering transformative solutions across multiple regions.