The problem
Running frontier AI models on GPUs is expensive, power-hungry and slow at scale
What they do
Custom AI inference chips and rack-scale clusters built to run large models faster and cheaper
Traction $1B+ signed customer contracts; first rack delivered to Jane Street Jul 2026
Hiring trend
- Open roles
- 116
- Ops roles
- 3
- Headcount
- ~450
Headcount ~150 (2025) to 400-450+ (mid-2026), roughly 3x in about a year; 115 open roles
Hiring in
- San Jose
- Taipei
- Austin
- Remote
Latest news
Oct 2026
Reviewing new funding offers at $40B-$50B valuation, two months after $21B round ↗Glassdoor
4.1 ★ 14 reviews ↗Funding
- Last round
- $700M
- Stage
- Series D
- Date
- Aug 2026
Led by Jane Street (Series D); Sequoia led the $300M Series C (Jul 2026)
- Hiring+2116 open roles on the live job board (30+ roles = +2)
- Funding recency+2Series D · $700M, Aug 2026 (2 months ago): 6 months or less = +1.5, $100M+ round +0.5
- Traction+1.5Strong growth: $1B+ signed customer contracts; first rack delivered to Jane Street Jul 2026
- Red flags0None found in the last 24 months
- Total5.52 + 2 + 1.5 − 0 = 5.5 → Sprint (4.75 or more)
Distance (10K) is a separate read on how long the lead can last. It doesn't change the score.
Distance
10K · edge: cheaper at size
Well-funded challenger with working silicon and $1B+ in contracts, but far behind Nvidia in inference compute, with one delivered customer so far, heavy burn, and dependence on scarce TSMC capacity.
Sources: GlobeNewswire: Etched raises $700M at $21B, first delivery to Jane Street (Aug 2026) ↗ · TechCrunch: Etched fields offers at $40B+, runway and burn context (Oct 2026) ↗
Last checked Oct 25, 2026