Staff-grade engineers, with the evidence to prove it.

StaffGrade is a technical search firm for Series A teams. Within 72 hours of one calibration call, you get a slate of senior and staff engineers — each backed by code, systems, and talks you can verify. Not keyword-matched exports.

Book a calibration call See a sample brief
72 hoursfrom calibration call to your First Slate of 10–15 researched candidates
Evidenceevery brief links to real work — repos, talks, systems — mapped to your bar
90 daysreplacement guarantee: if the hire doesn't stick, the next search is free
How it works

One call. Three days. A shortlist that holds up.

  1. Calibration — 45 minutes

    We extract your actual bar: the verifiable must-haves, the instant disqualifiers, and who your best engineer is and why. You sign off on a one-page role spec, and the clock starts.

  2. First Slate — 72 hours

    10–15 candidates, each with a one-page brief: evidence of technical depth with links, a fit table against your must-haves, and honest risks. You read each one in 90 seconds and tell us who's worth pursuing.

  3. Shortlist — inside 2 weeks

    We run personalized, human-signed outreach, screen the responders, and hand you 3–5 interview-ready candidates. You run your process; we keep the pipeline warm until someone signs.

The product

What a StaffGrade brief looks like

Illustrative sample — details fictionalized

Candidate M. — Staff Infrastructure Engineer, payments scale-up

Verdict: A — interview this week

Eight years across two infra teams, last four owning a payments platform's move from EC2 to Kubernetes at ~40k rps. Trajectory says platform owner, not feature engineer — which is what this role needs.

  • OSS: sole maintainer of a 2.1k-star Go connection-pooling library; issue history shows them debugging production deadlocks with users → maps to must-have #1 (Go at scale).
  • Talk: "Zero-downtime schema migrations" (2024) — mechanism-level, not tutorial-level; covers the failure modes your team hit last quarter.
  • Writing: engineering-blog post on the EC2→K8s migration, with real incident numbers.

Risks: no pgvector exposure (must-have #4 — not verified); 4 years at current employer, likely a harder pull; probable comp expectation at top of your band.

Who we place

Roles

  • Senior / staff backend engineers
  • Platform & infrastructure engineers
  • ML platform & AI infrastructure engineers
Who we work with

Teams

  • Seed to Series B, roughly 5–30 engineers
  • No internal recruiter — a CTO doing hiring nights and weekends
  • A role that's been open too long, and can't afford a mis-hire
Why StaffGrade

Built like an engineering team, not an agency.

Typical agency
StaffGrade
Keyword-matched resume dumps, 40 at a time
10–15 researched candidates, each brief readable in 90 seconds
"Strong background in distributed systems"
Links to the actual systems, code, and talks — verify it yourself
Automated sequences from a tool you can smell
Low-volume outreach, written per candidate, signed by a human
AI somewhere in the pitch deck
AI-leveraged research, human judgment on every call that matters — documented, audit-ready
Invoice, then silence
90-day replacement guarantee, engagement fee credited against the success fee
Who's behind it

Run by engineers, for engineering leaders.

I've spent eight years as a platform engineer everywhere from YC-backed growth-stage startups to Fortune 50 companies — scaling infrastructure through nine-figure ARR growth, cutting cloud spend by 60%, leading zero-downtime migrations of production data systems. The roles StaffGrade fills are the ones I've lived: infrastructure and platform seats that need an engineer who builds software, not a sysadmin with a cert. I know what these jobs actually demand, and what evidence proves someone can do them. Recruiters match keywords. I read the work.

— Habeeb Mohammed, Co-Founder

My co-founder Meike Buettner is StaffGrade's AI and full-stack arm. She's spent her career making production AI systems trustworthy — real-time LLM orchestration and streaming infrastructure at growth-stage AI startups, integrations across ten-plus model providers, and evaluation pipelines that grade AI output against structured human rubrics, most recently on a voice-AI platform answering live phone calls. At StaffGrade she owns the research and evaluation systems behind every slate — so when a brief says the evidence checks out, the checking was engineered by someone who builds evaluation systems for a living.