Hiring nowAI accelerators and hardware co-design

AI Hardware Verification Engineer

Also called Accelerator Verification Engineer

An AI Hardware Verification Engineer proves that accelerator logic and its interfaces behave correctly before silicon is committed. Escaped defects in a chip are costly and slow to fix, so this work protects the whole hardware programme.

What they do

Reads the architecture and micro-architecture specifications, then turns them into a verification plan. Builds testbenches in RTL simulation environments and writes stimulus for compute blocks, memory paths and external interfaces. Targets numerical edge cases such as rounding, overflow, special values and mixed precision. Tracks functional coverage, triages failures with designers and decides which gaps matter most. Runs regressions, reads waveforms and isolates root causes. Uses workload profiles to make sure tests reflect how real models will drive the hardware.

What a strong hire delivers

Within the first months, a strong hire delivers a clear verification plan for a defined block or subsystem, agreed with design. A working testbench follows, with regressions that run reliably. Coverage results show what has been proven and what has not. Early bugs are found and explained to designers with minimal reproduction. The team gains a shared view of risk and sign-off readiness.

How you can tell they are good

Ask them to walk through a verification plan they wrote and explain why they chose its coverage goals. Ask how they would test a matrix multiply unit for numerical correctness across precisions. Good candidates talk about corner cases, reference models and what they chose not to test. Be wary of anyone who only quotes coverage percentages without discussing bugs found.

Why it is hard to fill

Chip verification is a small field, and accelerator verification is smaller still. Most strong people are already embedded in silicon teams and are rarely looking. Titles vary: design verification, accelerator verification, validation. Candidates with CPU or GPU backgrounds often fit, but few have worked on numerical formats and AI workloads together. Founders often confuse this with software testing, which draws the wrong shortlist.

Where to find them

Look in design verification teams at chip companies, GPU and CPU groups, and networking silicon firms. Computer architecture and electrical engineering graduates who moved into verification are common. Some come from FPGA teams, and some from performance modelling who have drifted toward correctness work. Startups building accelerators also grow people with exactly this blend. Titles include design verification engineer, validation engineer and accelerator verification engineer.

Indicative pay

Not enough published evidence yet to give a fair range for this role, which is common for work this new. We benchmark it for you as part of a search.

Interview questions for an AI Hardware Verification Engineer

  1. Describe a verification plan you wrote for a compute block. How did you decide what to cover and what to leave out?

    A strong answer links the plan to the specification and to risk. It names coverage goals, reference models and trade-offs. Worry if they only describe tooling, or claim everything was covered with no sense of priority.

  2. How would you verify a multiply-accumulate unit that supports several numerical precisions?

    Look for reference models, directed tests for special values, rounding modes, overflow and accumulation order, plus constrained random stimulus. They should mention comparing against a trusted software model. Worry if they ignore numerical edge cases.

4 more questions, with what a strong answer covers, are in the Role Kit.

Job description you can post

We are building accelerator hardware for AI workloads and need a verification engineer to make sure our logic and interfaces behave as intended before we commit to silicon. You will work closely with architects and designers, and your results will shape sign-off decisions.

What you will do

  • Write verification plans for accelerator blocks and interfaces
  • Build and maintain RTL testbenches and regression flows
  • Develop tests for numerical edge cases across precisions
  • Measure functional coverage and close gaps with design
  • Triage failures and find root causes with designers
  • Use workload profiles to guide test priorities
  • Report verification status and risk clearly

What you will bring

  • Demonstrated RTL verification on a real design
  • Experience building testbenches and reference models
  • Strong grasp of computer architecture and digital design
  • Practical use of hardware simulators and EDA tools
  • Understanding of numerical formats and rounding behaviour
  • Clear written and spoken communication with designers

Nice to have

  • Experience with GPU programming or accelerator architectures
  • Familiarity with workload profiling tools
  • Background in simulation or performance modelling

Free to use and adapt. If it saves you time, a link back to this page is appreciated.

Skills that matter

  • Hardware and computer architectureAdvancedDesign computing structures and hardware interfaces
  • GPU programmingAdvancedOptimise parallel accelerator kernels
  • Linear algebra and optimisationWorkingReason about matrix operations and objective functions
  • Simulation and digital twinsWorkingBuild useful virtual representations of environments

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How we wrote this

Role definitions are our own editorial work, checked against occupational classifications, standards, research and employer pages. Titles vary by company, and this is a description of the work, not a job advert.

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