Why pre-screen AI hardware engineers before the technical loop
Accelerator design punishes the distance between a simulation and a running part. A design that meets timing in the tool can miss it in silicon, run hot enough to throttle away its advantage, or stall on memory bandwidth nobody budgeted for. Engineers who have taken a design through to hardware carry those scars and design around them. A short screen asks what actually ran, on what workload, and where it fell short of the projection.
What actually matters when screening AI Hardware Engineer candidates
- 01
Technical depth
Check depth on accelerator microarchitecture: systolic or dataflow MAC arrays, on-chip SRAM hierarchy, HBM3 bandwidth budgeting, INT8 and FP8 datapaths, and PPA trade-offs at a named process node.
- 02
Work that shipped
Ask which silicon or boards actually taped out or shipped: RTL they owned in SystemVerilog, emulation runs on ZeBu or Palladium, timing closure sign-off, and post-silicon bring-up results.
- 03
Diagnosis under uncertainty
Probe how they chased hard bugs: intermittent SerDes link errors, power delivery droop, thermal throttling, or a mismatch between RTL simulation and post-silicon behaviour.
- 04
Working across the org
Assess collaboration with compiler, kernel, and model teams: negotiating ISA or instruction extensions, board and package constraints with mechanical, and schedule pressure against foundry deadlines.
Pre-screening questions to ask AI Hardware Engineer candidates
12 questions grouped by what they test. Ask the same set in every screen and score answers on a consistent scale, or send them as an async video screen and compare answers side by side.
Designs that shipped
4 questions01Tell me about your experience with ASIC design for AI workloads.
Listen forTheir role in a specific part with the process node and whether it taped out, plus what the silicon did differently from simulation.
ASIC work that stopped at architecture, or a project described with no tape-out or measured result.
02What experience do you have with FPGA development?
Listen forDesigns that ran on a board with resource utilisation and achieved clock stated, not just synthesis results.
Designs that never left simulation, or timing closure described as something the tool handles.
03Can you describe a project where you implemented machine learning algorithms in hardware?
Listen forA specific model mapped to hardware with the quantisation and accuracy trade-off stated, plus the throughput achieved.
Accuracy loss from quantisation never measured, or throughput quoted with no batch or precision context.
04What are the key factors you consider when designing hardware architectures for AI applications?
Listen forMemory bandwidth and data movement treated as the primary constraint, with compute density argued against it rather than alone.
Architecture discussed only in terms of compute throughput, with data movement treated as a later detail.
Power and thermal
3 questions05What methods do you use for optimising power consumption in AI hardware?
Listen forPower budgeted from the start with measured results, and a specific technique that produced a number they can quote.
Power treated as a late-stage optimisation, or techniques listed with no measured saving.
06What techniques do you employ to manage thermal issues in AI hardware?
Listen forThermal behaviour under sustained load rather than peak, with throttling accounted for in the performance they claim.
Performance quoted at peak with no sustained figure, or thermal treated as a mechanical team problem.
07Describe a situation where you had to balance performance and cost in your hardware design.
Listen forA concrete trade-off with the decision and its consequence, including what performance they gave up and why it was right.
Cost treated as someone else's constraint, or a trade-off described with no decision actually made.
Finding the bottleneck
3 questions08What are the common bottlenecks in AI hardware, and how do you mitigate them?
Listen forBottlenecks identified from measurement on a real workload, with the mitigation and how much it actually recovered.
Textbook bottlenecks recited with no measurement, or optimisation applied before profiling.
09How do you approach debugging hardware-related issues in AI systems?
Listen forA specific bug with the instrumentation available named, hypotheses ruled out in order, and the confirmed cause.
Debugging described only through simulation, or a problem closed when the symptom stopped appearing.
10Can you discuss the role of memory hierarchy in optimising AI hardware performance?
Listen forData reuse and on-chip storage reasoned about against a real model's access pattern, with numbers attached.
Memory hierarchy explained in the abstract, with no application to a workload they worked on.
Working with software
2 questions11What challenges have you faced in hardware-software co-design, and how did you address them?
Listen forA real conflict with the software team, such as a kernel that could not use the design, and how it was resolved.
Hardware specified and handed over, or software described as failing to use the design correctly.
12What programming languages are you proficient in for hardware design?
Listen forA hardware description language with real depth, plus enough software fluency to read the kernels running on their design.
Languages listed with no design behind them, or no ability to read the software side at all.
How to score responses
Score every candidate on the same four criteria immediately after the screen. At this stage you are shortlisting for panel interviews, not making the final call.
Technical depth
35%5Explains their own datapath choices with real numbers: TOPS/W achieved, SRAM per tile, arithmetic intensity, and why HBM was chosen over LPDDR.
Work that shipped
30%5Names specific parts through tapeout and bring-up, describes their block, and cites yield, frequency, or MLPerf numbers on real hardware.
Diagnosis under uncertainty
20%5Walks a real debug from symptom to root cause using waveforms, scan dumps, or lab instruments, and states what the fix cost in area or power.
Working across the org
15%5Describes concrete co-design outcomes, such as changing an instruction after profiling kernels, and shows they held their ground with data.
A design that meets timing in the tool can throttle away its advantage in silicon, and both interview identically. A one-way video screen asks what actually ran.
Try it on HirevireScreening FAQ
Process basics
How long should a pre-screening round for an AI hardware engineer take?
Fifteen minutes across eight to ten questions, answered async. Enough to establish what reached hardware, test power and thermal thinking, and hear one bottleneck they diagnosed on a real workload.
How should I weigh FPGA against ASIC experience?
Decide what you are building first. Both are legitimate, and the constraints differ sharply: FPGA work rewards iteration speed, ASIC work rewards getting it right before tape-out. Someone strong in one may be slow in the other.
Evaluating answers
What is the strongest signal when screening an AI hardware engineer?
A design that ran and missed its projection. Engineers with hardware experience can say by how much and why: bandwidth, thermal throttling, a synthesis result that did not match. Simulation-only candidates report designs that always met target.
How do I judge hardware and software co-design answers?
Ask about a conflict with the software team. Real answers involve a kernel that could not use the hardware as designed, or a data layout argument. Anyone who describes the relationship as handing over a specification has not co-designed anything.
Go deeper on this role
Sanat has been hiring since 2012 and watching the recruitment industry change up close ever since, and turned that screening process into Hirevire's video screening platform. LinkedIn
