Use object-oriented programming (Java or C#), Salesforce Lightning, Force.com, Apex, SOQL, JavaScript, Git, Jira, IntelliJ, CI/CD, and Salesforce REST APIs. Hackajob Ltd is a platform that connects developers with job opportunities. This Salesforce Developer role delivers complex business and technical requirements on the Force.com platform.

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.

Building FPGA/ASIC applications with Hardcaml (OCaml RTL library), OCaml, Verilog, C, and vendor tools (Vivado, Quartus, Genus, Innovus). The team designs and implements new hardware applications and extends internal design/testing tools for Jane Street.

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.

Build enterprise-grade web applications using Python (Django/FastAPI), React, TypeScript, and PostgreSQL. Develop RESTful APIs, responsive UI components, and contribute to system design, CI/CD pipelines, and performance optimization.

You’ll work with Python (Django/FastAPI), React, TypeScript, PostgreSQL, REST APIs, and CI/CD. You’ll own end-to-end feature delivery on scalable, enterprise-grade web applications.

Develop, optimize and maintain high-performance C++ code for ultra-low-latency trading systems, working on strategy interaction with FPGA. Collaborate with infrastructure and network teams. Requires deep metaprogramming and 7+ years' C++ experience, with knowledge of memory management, multithreading, CPU cache optimization, and Linux.

Java (JDK 11+), multithreading, SQL/NoSQL databases (Oracle, MySQL, PostgreSQL, MongoDB, Cassandra), message queuing (Kafka, Tibco, Solace), Linux, JVM, and TDD. This tech lead role builds high-performance FX risk calculation systems for Citi’s Markets technology team, which supports global trading and risk management.

Work with Java, GitHub Copilot, Devin.AI, Citi Squad, Stylus Workspaces (Gemini, OpenAI, Claude, Llama), Docker, Kubernetes, and CI/CD. Build high-performance, real-time FX risk management systems for a greenfield platform with a 10+ year strategic commitment. As a VP, lead end-to-end delivery and technical direction.

You will work with C++17+, kernel tuning, network optimisation, and low-level profiling on Linux to build latency-critical HFT systems. The team designs end-to-end architectures for a tech-driven quant and systematic hedge fund, collaborating with FPGA, hardware, and infrastructure engineers.

You will work with C++17+ in a low-latency HFT environment, performing kernel-level tuning, network optimization, and systems programming. The team builds and operates ultra-low-latency trading systems for a global quant hedge fund, collaborating with FPGA and hardware engineers on end-to-end architectures where every nanosecond counts.

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.

Build ultra-low-latency, high-throughput systems using Rust in production, with additional exposure to C#, C++, FPGA, kernel bypass (DPDK, RDMA). The systems power trading across traditional markets and digital assets at firms operating in HFT, crypto exchanges, and DeFi infrastructure.

Work with Salesforce Lightning, Apex, SOQL, JavaScript, and the Force.com platform, with exposure to Git, Jira, IntelliJ, CI/CD practices, and Salesforce REST APIs. You will deliver solutions and manage complex business and technical requirements.

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.

As Multimodal Sensing and Fusion Engineer you will architect the multimodal perception front-end combining electro-optical, long-wave infrared cameras, radar, LiDAR, and inertial sensing using C/C++ or Rust and Python. Helsing builds software and AI that makes existing defence hardware more capable.

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.