Anthony Kelly

Are you ready to make online shopping smarter, faster, and more intuitive? We’re looking for a motivated Search Engineer to level up the search experience on our high-traffic eCommerce platform. What you’ll do:Architect and innovate with Elasticsearch, machine learning, and next-gen search techOptimize and personalize search relevance, ranking, and discoveryLead and mentor a talented team of engineersMeasure success with real impact using metrics like NDCG, F1, and CTRWhat you’ll bring:Strong skills in Java, Spring Boot, and PythonHands-on experience with Elasticsearch (or similar) and ML tools such as XGBoost, transformers, and LLMsExperience scaling high-traffic eCommerce or SaaS search systemsA can-do mindset and passion for solving complex problemsWhat you’ll love:An award-winning, people-first workplace cultureFlexitime and hybrid working options for better work-life balanceFreedom to choose your tech setupHealth and fitness perks, fresh local food, and even an in-house tennis courtIf you’re passionate about building world-class search experiences and want to join a team that values innovation and balance, we’d love to hear from you.

Implement high-performance AI inference solutions on FPGA, contributing to our SystemVerilog IP library and delivering complete FPGA designs for deployment in modern data centers. Role: (Senior) Software DeveloperSalary: up to £90,000Location: Cambridge (onsite 3 days per week) Working closely with software and ML engineers, you’ll integrate against compilers, runtimes, and drivers to build reliable, low-latency, high-throughput applications. You’ll design reusable, parameterizable IP for the latest AI-capable FPGAs, package full solutions, achieve timing closure through floorplanning, and test designs on hardware. You’ll also extend verification environments, make low-level software interface changes, and help shape coding standards and Agile workflows. You’ll bring:5+ years of SystemVerilog/Verilog/VHDL experienceStrong FPGA EDA tool knowledge (Quartus, Vivado)Experience in FPGA bring-up, timing closure, and Linux dev environments

Design and build software systems that run ML inference workloads with state-of-the-art efficiency on FPGA-based accelerators. Role: (Senior) Software DeveloperSalary: up to £90,000Location: Cambridge (onsite 3 days per week) You’ll work across the stack, from co-designing instruction sets with hardware engineers to developing compilers, APIs, and runtime libraries for seamless ML integration. The role involves systems programming, compiler development, and creating tools for debugging, profiling, and optimisation. Our stack is primarily written in Rust, chosen for its performance, safety, and mature tooling. We’re a small, collaborative team with deep technical expertise, tackling some of the biggest challenges in ML today. We focus on end-to-end optimisation, from algorithms down to silicon, to deliver world-leading performance with low power consumption. You’ll bring:3+ years in systems programming, performance-sensitive code, or compiler devExperience with Rust (or C/C++/functional languages)Strong problem-solving skills and enthusiasm for novel architectures

Job Title: MLOps EngineerLocation: Paris, Munich or remote in EuropeYour mission is to to make AI more efficient, accessible, and sustainable. We build tools to help organizations develop AI with minimal cost and carbon impact.Manage and optimize ML infrastructure and SaaS platform. You’ll develop backend services, basic front-end interfaces, and contribute to cloud platform for model compression and benchmarking. You’ll manage our AWS clusters (For now - open to change), ensuring scalable, reliable compute for ML workloads, and collaborate with ML engineers, software teams, and our go-to-market group.Required Skills:Strong programming skills (Python, C++, Java)Experience with cloud platforms, containerization, CI/CD, and IaC (Terraform/CloudFormation)Familiarity with ML algorithms and pipelinesStrong collaboration and ownership mindsetBonus: Experience with deep learning frameworks or open-source MLOps.