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Normal Computing

Develops probabilistic AI for enterprise applications

Hardware Engineer - FPGA

Silicon & ASIC DesignConfirmed live in the last 24 hours
Full-timeLondon, UKNo salary listedIn-person

About the Job

Requirements

  • Proven industry experience taking FPGA designs from RTL through timing closure to validated hardware, ideally in an ASIC prototyping, emulation, or high-growth hardware environment.
  • Expert-level SystemVerilog and/or VHDL for synthesis, with deep proficiency in Xilinx Vivado, including synthesis, place and route, timing closure, and the IP catalog.
  • Hands-on experience implementing and debugging PCIe, plus AXI/AHB, SPI, UART, JTAG, and other common interfaces.
  • Strong Python for automation, test, and build tooling.
  • Strong board-level bring-up and lab debugging skills on real hardware.
  • Ability to work independently, pivot quickly, and address ambiguous problems spanning hardware, software, and physics.

Responsibilities

  • Lead the selection, procurement, and bring-up of FPGA prototyping platforms, such as HAPS, VCU118/VPK180-class boards, or custom hardware, for pre-silicon RTL validation and software development.
  • Adapt and implement complex ASIC RTL onto FPGA targets, including multi-clock-domain architectures, CDC bridges, and timing closure on dense designs.
  • Integrate in-house designs with third-party and vendor IP, serving as the expert on the AMD/Xilinx ecosystem, including Vivado IP Integrator, transceivers, and memory controllers.
  • Design, implement, and validate high-speed I/O, focusing on PCIe for accelerator cards in standard servers.
  • Build FPGA-based tester designs for silicon bring-up, device characterization, and automated test environments.
  • Develop the software layer around the hardware, including Python/C++ hardware abstraction, register-map generation, and automated build and regression flows.
  • Root-cause timing and functional issues in real time using ILA/Vivado Analyzer, oscilloscopes, logic analyzers, and BER/eye-diagram characterization of high-speed links.

Desired Qualifications

  • Experience with verification frameworks such as Cocotb or UVM.
  • Experience deploying ML models to FPGAs using hls4ml, FINN, or custom NN-to-RTL flows, or building real-time, sub-microsecond signal processing pipelines.
  • Mixed-signal ASIC exposure, including digital front-ends, ADC/DAC interfaces, or analog compute.
  • SERDES tuning and signal integrity fundamentals.
  • CI/CD for hardware, including GitLab CI and Docker-based build and test.

About the Company

Normal Computing

Develops probabilistic AI for enterprise applications

Normal Computing builds AI systems for large, mission-critical workloads and provides generative AI tailored for enterprises. It uses Probabilistic AI, which uses statistical analysis to predict outcomes, giving firms more control over reliability, adaptability, and auditability. The company differentiates itself through leadership from Google Brain, Palantir, and X, and by focusing on risk-aware, enterprise-grade AI for industries with strict risk constraints. Its goal is to help Fortune 500 companies and government agencies deploy AI in critical operations with strong reliability, clear audit trails, and manageable risk.

Company size
51-200
Company stage
Early VC
Total funding
$133M
Headquarters
New York City, New York
Founded
2022

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