Optimize ML inference for edge accelerators and GPUs using TensorRT, CUDA, Qualcomm QNN, Triton, OpenCL, MLIR, ONNX, Python, and C++. You will run large transformer-based models efficiently on low-power edge devices to enable Wayve’s first driving product.

You will use Python, Trio, PyTorch, TensorFlow, JAX, Kubernetes, Rust, C++, and GPU clusters. The team advances reinforcement learning for large language models, improving Claude’s autonomy, coding, and reasoning capabilities.

Research Engineer will work with large-scale ML systems, GPUs, Kubernetes, and PyTorch to optimize attention mechanisms, compare Transformer variants, and scale distributed training. They conduct research on model architecture and algorithms for the Pretraining team at Anthropic, a company building beneficial and safe AI systems.

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 will work with GenAI tools like OpenAI, LangChain, Hugging Face, Vertex AI, and Bedrock, plus Python, PyTorch, TensorFlow, vector databases (FAISS, Pinecone), and cloud platforms. The Data Science Team builds and deploys LLM-based applications for enterprise use cases such as customer service, document automation, summarization, and knowledge retrieval.

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 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.

You will use Python, pandas, scikit-learn, PyTorch, and Hugging Face to wrangle messy legacy data and build production-ready AI models. The team is building an AI operating system for industries with fragmented, complex data, shipping models for forecasting, optimisation, and recommendation.

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 will engineer agentic AI decision loops, retrieval and embedding pipelines, and rigorous testing frameworks using Python, orchestration frameworks, vector databases, and semantic search, with JVM languages as a plus. Own the full model lifecycle, balancing fine-tuning specialized models versus frontier LLMs, while mentoring on production-grade ML standards. This is a senior ML engineering role for iForce Connect, building deep, integrated AI systems that navigate complex datasets.

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.

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++.