The role involves developing embedded software and software for high-speed network platforms, working with GPU-based infrastructure and interconnect technologies. The company builds next-generation networking solutions integrating hardware and photonics for high-performance computing systems.

Quantitative Researchers at Jane Street build models, strategies, and systems for pricing and trading financial instruments, working with Python, petabytes of data, and a large GPU cluster. They apply statistical and ML techniques, from linear models to deep learning.

DeepL★3.8 (4)· London

Work with Kubernetes, Terraform, ArgoCD, Go or Python, and AWS across hybrid on-prem and cloud infrastructure. The Hybrid Platform Engineering track builds and operates the compute layer—Kubernetes control plane, networking, storage, and core services—powering DeepL's language AI products and research. The role involves deepening the hybrid model and unifying workloads across on-prem and AWS.

You’ll train ML models, work on ML libraries and workflows, and use a GPU cluster with H100/H200/B200s. Projects are real-world machine learning applications for Jane Street’s global trading business, testing ideas against noisy financial data. No team size is stated.

Senior Machine Learning Engineer at ASOS, building production ML systems for personalized outfit recommendations and style discovery. You'll work with deep learning frameworks like PyTorch/TensorFlow, distributed GPU infrastructure, and MLOps practices including CI/CD, containerization, and observability. Collaborate with ML Scientists on batch and real-time models serving millions of customers.

Use deep learning frameworks, distributed computing, GPU-based training, and MLOps to build and deploy batch and real-time ML models. The Search & Recommendations team develops personalised recommendations, product ranking, and AI styling experiences for ASOS's online fashion platform, helping customers discover products.

Design, build, and optimize large-scale HPC/AI infrastructure using GPU optimization (CUDA, NCCL, GPUDirect RDMA, NVLink). You'll work with triple-digit petabyte-scale storage and massive CPU/GPU clusters, using Python, C/C++, and Linux in globally distributed data centers. This role covers HPC cluster design, performance tuning, and automation for thousands of nodes.

Zanders★4.5 (2)· London

Work with modern C++ (STL, templates, RAII, unit testing, build systems); Python or Java; Linux; GPU compute (CUDA, ROCm); and HPC tools (MPI, Slurm). Lead quantitative engagements designing, developing, and validating models for market risk, counterparty credit risk (CCR), CVA/XVA, margin models, and derivatives pricing across asset classes. Act as a career coach for up to three direct reports.

Imperial College London★4.2 (238)· London

Work with PyTorch, GPU kernel programming, Verilog, and NVIDIA's Holoscan Sensor Bridge to integrate FPGA-based AI-ISP accelerators with edge GPU platforms. The project targets low-latency, high-throughput sensor-to-GPU streaming for the "Sense-Decide" pipeline in edge automation.

Linux Recruit★3.0 (1)· London

Work with PyTorch, distributed training libraries, Slurm, Kubernetes, and AWS multi-node GPU clusters to architect and optimize large-scale model training pipelines. You will join a stealth AI startup at the intersection of robotics and embodied AI, building the infrastructure that powers next-generation AI breakthroughs.

Work with Python, PyTorch (or JAX/TensorFlow), RL fine-tuning frameworks like TRL or verl, and distributed multi-GPU training. The Reinforcement Learning Team advances RL, Bayesian optimisation, AI agents, LLMs, and vision-language models, applying them to AI for science, chemistry, physics, and robotics while publishing at top venues.

You will work with Python, PyTorch, PyTorch Lightning, TensorFlow, and experiment tracking tools to design and implement custom machine learning model components for Search, Retrieval, Ranking, or Content AI. You will join the Tripadvisor Experiences R&D Team, which is evolving the marketplace for travel experiences by powering how millions of users discover and plan itineraries. You will act as a technical lead and pod architect, and mentor mid-level and associate ML scientists.

You will use Python, KQL, SPL to build detection logic and automate response. You will protect Writer’s AI platform, training data, and model deployments, detecting threats like prompt injection and model extraction. You will coordinate incident response across teams and hunt for threats in GPU clusters and distributed training environments.

Work with Nvidia GB200 72NVL CX8 and AMD MIxxx architectures, coding in C, C++, C#, Java, JavaScript, or Python to benchmark, profile, and debug GPU clusters. You will help build foundational models powering Microsoft’s personalized AI assistant, Copilot, spanning training and inference infrastructure, model architecture, and data curation.

You'll work with PyTorch, the NVIDIA ecosystem, and ML infrastructure across the full model lifecycle. You'll create deep-dive articles, benchmarks, walkthroughs, and reproducible technical assets while representing the company at community events and collaborating with its internal AI Lab and partners including vLLM and SGLang.

You will design, build, and operate AWS infrastructure, engineer Kubernetes environments (EKS, Docker, Helm), and use Terraform for infrastructure-as-code. You will support critical investment and trading systems, improve CI/CD pipelines with GitHub Actions and GitOps (Argo CD), and work with databases like Snowflake, Aurora, and SQL Server. You will take end-to-end ownership of production systems, solve complex problems across distributed environments, and influence cloud architecture and reliability standards.

Tripadvisor★3.9 (142)· London

You will work with Python and PyTorch/TensorFlow, leading the ML strategy for Search, Ranking, and Content AI. As a technical lead and pod architect on the Tripadvisor Experiences R&D team, you will evolve the core discovery engine for their travel experiences marketplace and formally mentor mid-level and associate ML scientists.

You'll work with PyTorch, distributed training, Docker/K8s/IaC, and Python backend. The company builds foundation models for extreme physics to accelerate progress in semiconductors, aerospace, defence, and fusion. Joining a small, research-heavy team, you'll be the first dedicated engineering owner for the ML stack.

Work with Python, PyTorch, scipy, scikit-learn, numpy, pandas, MLflow, Docker, Kubernetes, Ray, and GCP to build and scale distributed compute for research workloads. Phaidra creates AI-powered control systems for the industrial sector using reinforcement learning to enable facilities to learn and improve automatically.

You will own the C++17+ systems that ingest, process, and store high-rate sensor data from metal 3D printers, serving physics-informed ML inference and 3D insights in production. The work involves Python bindings (via pybind11), WebSockets, CMake, 3D geometry/meshes, and on-premise deployment. Nexus is an early-stage startup building the data and software layers for additive manufacturing of safety-critical parts.

Apple★4.1 (14k)· London

Work with GPU architectures, C/C++, Python, and GPU APIs like Metal, CUDA, Vulkan or DX12. You will develop GPU workloads, automated flows, and tools for verifying Apple silicon graphics processors, supporting teams in pre- and post-silicon design bring-up.

Nebius★0.0· London

You will work with Linux kernel, KVM, QEMU, device drivers, firmware, ultra-high-bandwidth networking, and NVIDIA GPUs (GB300, H200, VR200). The Compute Systems Engineering team develops and operates the core virtualization platform and HPC/GPU cloud deployments, owning the full systems stack. This role coordinates complex programs across hardware, software, infrastructure, and external partners.

Apple★4.1 (14k)· London

Work with Kubernetes (GKE/EKS), GCP/AWS, Java, Swift, Python, or TypeScript, Nginx, Envoy, Prometheus, Docker, Terraform, Pulumi, and CI/CD pipelines. The SRE team supports Private Cloud Compute (PCC), Apple's privacy-preserving cloud intelligence platform, ensuring availability and automation of critical systems.

You will work with communication libraries like NCCL, MPI and NVSHMEM, and profiling tools such as Nsight Systems, PyTorch Profiler and perf. The team focuses on improving the networking and communication layer behind large-scale LLM training, optimizing collective communication performance across distributed GPU clusters.

You'll work on distributed training, GPU optimisation, large-scale serving, and low-level infrastructure including kernels, profiling, and parallelism. Your role is to build and run training and inference infrastructure, owning GPU clusters, throughput, and reliability for frontier labs, fintechs, and enterprise-AI teams.