HSBC★4.0 (16k)· London

Work with Python, Docker, Kubernetes, Flask/FastAPI, PyTorch/TensorFlow, SQL Server/Postgres, and CI/CD pipelines to build and deploy production-grade ML and GenAI services. You will join the Growth AI programme within HSBC’s Corporate & Institutional Banking Data & Analytics, developing AI applications to identify business opportunities and mitigate client attrition.

Work with LangChain, RAG, FastAPI, React, DuckDB, GCP, Python, and production LLMs. Build agentic AI systems automating digital forensics and incident response investigations for an AI-native security company. This is a 0-to-1 founding role shipping across the full stack.

The role involves Python, FastAPI, Docker, Kubernetes, CI/CD, MLOps tools (MLflow, Weights & Biases), and workflow orchestration (Airflow, Prefect, Kubeflow) for deploying and monitoring ML models in production. You'll build and maintain ML infrastructure, pipelines, and observability for an edge computing environment at a company scaling its ML platform.

Lightning AI seeks AI Platform Support Engineers to support ML engineers running large-scale training and inference workloads on Kubernetes and GPU platforms. You'll diagnose distributed PyTorch failures, GPU orchestration issues, and infrastructure problems using tools like Prometheus and Grafana, while partnering with customer engineering teams on production reliability. Required: strong systems troubleshooting, Kubernetes experience, Linux expertise, hands-on ML infrastructure operations with PyTorch and CUDA, and excellent communication with technical customers.

You will work with JAX, PyTorch, TensorFlow, ADK, MCP, agentic frameworks, LLM ecosystems, and GCP. Isomorphic Labs applies frontier AI to drug discovery, building on AlphaFold to accelerate rational drug design with predictive and generative AI models. You will secure the AI-first platform and autonomous agentic workflows that power the drug discovery pipelines.

Senior Applied Researcher uses Python, NumPy, pandas, SciPy, scikit-learn, PyTorch, TensorFlow for deploying ML systems in production cloud environments. The Monolith team applies machine learning to solve physics and engineering challenges for industrial products like automotive systems, aircraft, and advanced batteries.

Use Python, NumPy, pandas, SciPy, scikit-learn, PyTorch/TensorFlow, Spark/Ray/Dask, SQL, and Kubernetes to build a layered reliability platform for proactive reliability engineering, improving GPU utilization and system efficiency in production environments.

Work with Python/TypeScript, FastAPI/Flask/Express/NestJS, AWS/Azure/GCP, Kubernetes/Docker, Terraform, PostgreSQL/DynamoDB, and CI/CD tools. You will build core infrastructure for the Scale Generative AI Platform (SGP), designing scalable APIs, distributed data systems, and deployment pipelines for enterprise GenAI production

You will work with Python, FastAPI, Node.js, Docker, Kubernetes, and AWS to build shared backend services, frameworks, and developer tooling. This role designs, builds, and maintains the platform foundations that internal engineering teams use to build, integrate, and deploy software.

Work with Python, machine learning, statistical modelling, and optimisation on large-scale GPU infrastructure telemetry, building a layered reliability and intelligence platform that shifts CoreWeave from reactive troubleshooting to proactive reliability engineering. The role involves designing models for anomaly detection, failure prediction, workload optimisation, and agentic root cause analysis.

You will build telemetry pipelines and dashboards using Python, Golang, Kubernetes, Prometheus, Grafana, gNMI, and SNMP across platforms including Arista EOS, NVIDIA Cumulus Linux, and Nokia SR OS. The Network Observability team designs and maintains monitoring systems for CoreWeave’s global GPU cloud network, enabling real-time anomaly detection and automated alerting.

You'll work primarily with React and TypeScript on the frontend, and Python (FastAPI/Flask) on the backend, using tools like Storybook. The Monolith AI Engineering Team builds the core platform for engineering simulation and AI workflows, aiming to become a super-intelligent AI test lab for the engineering industry.

Axon★3.3 (126)· London

You will work with Python/C++, PyTorch, TensorFlow, AWS, AzureML, and edge hardware (Qualcomm, NVIDIA, Intel) using TensorRT, ONNX, and SNPE. As part of Axon’s CoreAI team, you will drive end-to-end development of AI systems across cloud, edge, and robotics platforms, building production systems for computer vision, NLU, multimodal AI, and GenAI.

Axon★3.3 (126)· London

Work with LLMs, MLLMs, Computer Vision, and GenAI using Python, C/C++, TensorFlow, PyTorch, or Keras and ROS. As part of Axon's AI team, advance state-of-the-art models for intelligent reasoning and perception of multimodal data, deploying them in cloud, devices, and robotics. Provide technical leadership to junior scientists.

GSK★4.1 (5.4k)· London

You'll design, build and operate scalable cloud infrastructure and model-serving systems supporting AI/ML workloads at GSK using Python, FastAPI, GCP (Cloud Run, GKE, Cloud SQL), and major deep learning frameworks. The AI for Science team develops production-grade software enabling drug discovery and personalized medicine through state-of-the-art AI and machine learning applied to biomedical data.

You’ll work with Python/TypeScript, FastAPI/Flask/Express/NestJS, AWS/Azure/GCP, Kubernetes, Docker, Terraform, PostgreSQL, and DynamoDB. You’ll build backend infrastructure for Scale’s Generative AI Platform, delivering APIs and distributed systems that bring GenAI into production for large enterprises.

Nebius★0.0· London

You'll work with GPU computing, InfiniBand networking, KVM/QEMU virtualization, Kubernetes, and Linux kernel systems, using C/C++, Go, or Python. The GPU & InfiniBand team optimizes Nebius's hyperscaler cloud platform for AI workloads, focusing on performance tuning, hardware integration, and infrastructure automation across multi-GPU HPC environments. You'll troubleshoot root causes, enhance monitoring automation, and configure device fabrics to support new GPU hardware at scale.

You'll work with Azure, Terraform, Kubernetes, Docker, CI/CD tools (Jenkins, GitLab CI, GitHub Actions), Python, and monitoring platforms like Prometheus and Grafana to build and scale infrastructure for Orbital Copilot, an AI assistant that accelerates commercial real estate due diligence for law firms. As the second SRE, you'll design cloud infrastructure, implement container orchestration, develop automated pipelines, and establish reliability practices from the ground up in a startup environment.

Faculty★0.0 (16)· London

You'll work with PyTorch, TensorFlow, HuggingFace, and Python to conduct AI safety research, including red teaming and evaluations for frontier language models in high-risk domains like cybersecurity and CBRN. Faculty partners with leading model developers and national security institutes to advance safety understanding through technical research, publications, and thought leadership. You'll lead a small, high-agency research team shaping Faculty's AI safety agenda and real-world safe AI deployment.

Humanoid★0.0· London

You'll work with Python, TensorFlow, PyTorch, AWS/GCP/Azure, Docker, Kubernetes, GitHub Actions, GitLab CI, ArgoCD, Terraform, and Helm to build and maintain CI/CD pipelines, ML infrastructure, and deployment systems. Humanoid develops commercially-scalable humanoid robots, with the Data & Compute Platform team supporting ML model lifecycle management and production infrastructure for their HMND-01 Alpha platform running in industrial pilots.

You'll work with Kubernetes, Slurm, Python, C++, PyTorch, and AWS to build and optimize large-scale AI training and inference clusters at Perplexity. Responsibilities include designing scalable Kubernetes deployments, managing Slurm-based HPC environments for distributed LLM training, developing orchestration APIs, implementing resource scheduling systems, and building monitoring solutions for ML workloads. The role requires expert Kubernetes administration, Slurm proficiency, distributed systems expertise, and ideally 3-5 years of ML infrastructure experience.

You'll work with PyTorch and modern deep learning frameworks to architect and scale distributed training infrastructure for autonomous decision-making systems. At Helsing, a defence AI company, you'll handle high-volume data processing, reinforcement learning, and foundation models—extending integrated frameworks, optimizing large-scale distributed training, and designing data strategies for GPU efficiency across cross-functional teams.

Anthropic★5.0 (1)· London

You'll work with Python, PyTorch, JAX, and async frameworks like Trio to build reinforcement learning infrastructure and train agentic models at Anthropic, focusing on computer use, code generation, and reasoning capabilities for Claude. The role blends research and engineering, requiring you to architect distributed training systems, design novel RL environments, and optimize performance across GPU clusters while collaborating with alignment and applied teams.