Velaura AI is not a fresh startup, and that is the first thing to know before you interview. It is the AI-focused rebrand of Auradine, the Santa Clara semiconductor company that built bitcoin-mining chips before changing direction around March 2026. In August 2026 it announced a $110 million Series A at a valuation above $1 billion, led by Seligman Ventures, with new investor Capricorn Investment Group joining returning backers including Mayfield, MARA, Premji Invest, and Samsung Catalyst Fund. Co-founders Rajiv Khemani (CEO) and Manu Gulati lead a team drawn from Apple, NVIDIA, Google, Qualcomm, and Marvell.
The product is two things, and interviews conflate them
Coverage blurs two separate offerings, and a candidate who keeps them straight is already ahead. The first is Titan Core, a silicon design and IP platform, not a chip. Velaura takes a customer’s existing design for the power-hungry math blocks, the multiplier-accumulators and matrix-multiply units that do most of the arithmetic and burn most of the watts, and re-implements them with proprietary low-voltage standard-cell libraries, custom circuit techniques, hardened logic, and error detection and correction run through specialized EDA flows. What comes back is an optimized physical layout that drops in for the hungriest part of the chip, with no change to the customer’s software. The claim is up to 2x lower overall chip power, up to about 500W saved on a 1,000W GPU or XPU, and 2-4x better performance per watt on the math itself, achieved by running those blocks at a much lower supply voltage, roughly 300 to 500mV, while staying above the transistor’s switching threshold.
The second offering is Velaura’s own silicon. Several reqs name “Velaura’s next-generation Physical AI SoC” and “our NPU,” so alongside the data-center IP business the company is designing its own low-power SoC for physical AI, the robots, drones, and autonomous systems where the power budget is a battery. Most of the open engineering roles feed that SoC. Know which motion a given role belongs to, because the compiler and platform-software work only makes sense against the in-house NPU, not the drop-in IP.
Read the “30 million ASICs” claim correctly
Velaura cites deployment across 30-plus million production ASICs. Read that as manufacturing and low-voltage-methodology pedigree from the Auradine mining era, not as evidence that the AI product is widely adopted yet: as of the funding announcement there were no named AI customers and no independent verification of the performance numbers. That cuts both ways, and it is a near-certain interview topic. This team has closed timing, hit yield, and shipped parts at volume, so interviews assume you can talk about silicon that actually has to work; the open question is commercial, whether the low-voltage techniques that won in mining transfer to AI accelerator and NPU workloads. Expect some version of “what carries over from the prior product and what doesn’t,” and have a view.
What is confirmed, and what is modeled
Be clear about the evidence, since it decides what any guide including this one can claim. Velaura lists 36 open roles on its Lever board, most of them engineering, and under California pay-transparency rules most US postings carry a salary range. Those postings, their required-skills text, and their bands are confirmed. The board skews senior, most titles are senior, staff, lead, principal, or architect, so a candidate around three years in should target the handful of individual-contributor reqs that post the lower band, the RTL Designer, the RTL Engineer for the memory subsystem, and a Design Verification Engineer role, rather than the leadership titles. None of the US postings mention visa sponsorship either way, so if you need it, confirm with the recruiter on the first call rather than assuming. What is not published anywhere is the interview process itself: there are no first-hand candidate write-ups of a Velaura loop, which is expected months after a rebrand. So the stage-by-stage loop below is modeled on how AI-silicon and physical-design startups like Tenstorrent, Groq, and d-Matrix run their loops, not a Velaura script, and it is labeled as such every time it appears.
Why a low-power thesis shapes the questions
The product defines the interview. When your entire pitch is performance per watt, power stops being an afterthought and becomes the thing every technical round returns to, whether you meet it as near-threshold margin, clock gating, roofline tradeoffs, or quantization. None of this needs insider knowledge; it falls out of what Velaura says its technology is.
The table groups the role families Velaura is actively posting, the confirmed signal from Lever, and what a loop for each most likely probes. The last column is informed inference, not a published Velaura process.
| Role family at Velaura AI | Confirmed open roles (Lever, Sept 2026) | Posted US base range | What a loop most likely probes (modeled on comparable silicon startups; not a published Velaura process) |
|---|---|---|---|
| RTL and digital design | Senior RTL Engineer, RTL Lead, RTL Power Engineer, RTL Designer, RTL Engineer Memory Subsystem | $150,000-$250,000 (IC), $200,000-$300,000 (senior/lead), plus equity | Verilog/SystemVerilog depth, microarchitecture and memory-subsystem tradeoffs, clock gating and power domains, lint and CDC discipline, ownership through tapeout |
| Design verification and formal | Multiple DV roles (AI Accelerator, CPU, SoC), Senior/Formal Verification, Senior Emulation | $150,000-$250,000 (IC), $200,000-$300,000 (senior/lead), plus equity | Constrained-random and coverage-driven verification (UVM), formal properties, emulation bring-up, finding the bug before silicon does |
| Physical design and backend | Physical Design, Physical Verification, DFT, STA/Synthesis, Custom Circuit Design (Bangalore) | Not posted for Bangalore roles | Timing closure and signoff at low voltage, near-threshold characterization, IR drop, DFT, custom circuit and standard-cell design, error detection/correction |
| Silicon architecture | AI Systems Architect, Power Management Architect, Functional Safety Architect, Security Architect, Performance Modeling Architect | $200,000-$300,000 base plus equity | Perf-per-watt and roofline reasoning, power-delivery and DVFS architecture, functional safety for physical AI, threat modeling, workload performance models |
| Compiler and toolchain | Accelerator Compiler and Tool Chain Lead, CAD/EDA infrastructure roles | $200,000-$300,000 base plus equity | Model lowering to the NPU: graph import, operator lowering, compiler IR and transformations, quantization integration, graph partitioning, code generation |
| Platform and systems software (Physical AI) | Principal AI SoC Runtime Software Architect, Platform Software Lead – Physical AI | $200,000-$300,000 base plus equity | Runtime and driver depth, memory and DMA management, robotics middleware, low-latency deployment where a missed deadline is a safety event |
On shape, a silicon loop like this usually opens with a recruiter screen that sorts you into a track, then a hiring-manager or architect conversation about a project you owned, two or three technical rounds in your discipline, and a values round with a founder or director. That is the modeled spine, drawn from the companies above; ask your recruiter for the real one on the first call, since the process is still being standardized.
If you are on the silicon hardware track
This is the bulk of the open reqs, which fits a team that has taped out before. The Senior RTL Engineer posting is the clearest confirmed signal: it wants you to turn architectural concepts into production silicon for Velaura’s Physical AI SoC, owning significant portions of the design and driving microarchitecture alongside architects, performance modelers, and verification. So expect depth rather than trivia: how do you size a memory subsystem so the compute engine never starves, and what do you trade in area or power to close timing on a block? Physical-design, custom-circuit, DFT, and STA candidates should expect the matching depth in their discipline. Because Velaura’s whole claim is voltage, the low-power version of every question is fair game: push a block from 700mV toward 400mV and what breaks first, and how do timing margin and error correction let you run there safely? Make every answer a power argument as well as a correctness one.
On format, Velaura has not published its rounds, so expect the shape these take across the field. An RTL round is usually live design on a shared editor or whiteboard: build something small and real, a synchronizing FIFO, a round-robin arbiter, or a clock-domain-crossing bridge, then defend the reset and timing corner cases. A microarchitecture round is discussion of a block you shipped and would build differently now. Physical-design, STA, and custom-circuit candidates tend to get a paper exercise on a timing path or a constraints-and-flow walkthrough rather than coding, and DV candidates a testbench and coverage-plan discussion, sometimes writing SystemVerilog or UVM live.
If you are on the compiler or systems-software track
The Accelerator Compiler and Tool Chain Lead posting maps to Velaura’s own NPU, not the drop-in IP. It asks you to own the path from a customer’s AI model to an optimized executable for the NPU: graph import, operator lowering, compiler IR and transformations, quantization, graph partitioning, and code generation, with a background shipping compiler infrastructure for ML accelerators, GPUs, or DSPs. So prepare to lower a model graph out loud: capture it, pick an IR, schedule and fuse, plan memory movement, and keep numerics correct when you quantize. On an operator bound by reading weights out of memory, do you fuse two ops to save a round-trip, or keep them apart for a cleaner tiling that vectorizes better, and what does the arithmetic intensity say? The posting names the tasks but not a specific language or framework, no MLIR, LLVM, TVM, or C++ called out, so prepare on the concepts and bring whatever systems language you are strongest in; C++ is the safe default for accelerator-compiler work. On format, expect live coding, a small graph transformation, an operator scheduler, or memory allocation over a toy IR, or a take-home pass, then a design discussion of the lowering stack.
The platform-software roles, the Principal AI SoC Runtime Software Architect and the Platform Software Lead for Physical AI, sit at the intersection of silicon, runtime, and robotics middleware, and the postings say you will help define the architecture, not build on a fixed one. Expect embedded and RTOS depth, memory and DMA management, and low-latency scheduling, with a robot’s constraint added: a real-time deadline is a safety event, not a dropped frame. Rounds here usually pair a C or C++ coding problem with a runtime or driver design discussion. General system design prep transfers, but ground it in a device and a power budget rather than a web service, and keep your complexity analysis sharp, because a slow runtime loop wastes the watts the silicon just saved.
How to read the comp
Velaura posts real ranges, so you guess less than at most startups. Senior and lead US roles list a $200,000-$300,000 base plus equity, while the individual-contributor RTL and DV titles sit at $150,000-$250,000, and the Bangalore backend roles post no range at all. California pay-transparency rules are why the US figures exist and the India ones do not. The larger and murkier piece is equity. At a valuation above $1 billion the grant is real but illiquid, and because the AI business has no publicly confirmed customers yet, its value is a bet on execution rather than cash you can touch. At an offer, pin down the instrument (options or RSUs), the strike or grant price, the share count against total shares outstanding, the most recent 409A, and your level, since level drives the number more than negotiation does. A company this early in its AI rebrand has no levels.fyi history yet, so lean on the posted band and ask the recruiter for the range at your target level, then model the whole package as a range with a total-comp calculator and walk through a salary negotiation plan before you counter, since an early-stage silicon company usually has more room on equity than on base.
How to prepare
Prepare for your exact track: hardware candidates on RTL and microarchitecture, verification methodology, or timing and power at low voltage, plus a crisp story about designing for watts; compiler and systems candidates reasoning from a model down to the NPU and back. A behavioral round is close to certain even with the loop unpublished, since a team staffing a tapeout screens for ownership, so keep two or three tight stories ready with the STAR method. Run your resume through a resume checker so the silicon or compiler keywords survive an ATS parse, and a short study plan keeps the fundamentals warm. If you are weighing Velaura against the field, the AI-startup interview guides and the difficulty index put it next to its peers, and the physical-AI side of its market overlaps with Physical Intelligence and Skild AI, whose robots are the kind of power-starved system Velaura’s silicon targets. To see the loop next to other companies, browse the full set of company interview guides.
Velaura’s interview is inferable from its postings and its history, not from transcripts that do not exist yet, which favors the candidate who does the reading. Show up able to separate the Titan Core IP business from the in-house Physical AI SoC and to argue why performance per watt is the whole game, and you have answered the question Velaura cares about most before anyone asks it.
Practice the behavioral round:
