AI systems · Inference · GPU orchestration · MLOps
Hire AI infrastructure engineers
A search for founders who need to build, scale or govern an inference stack. Those are three different hires. Settle which one, the evidence, the decision owner and the sequence before the search opens.
You speak with Emily Landon directly. Guarantee terms are confirmed on the call.
- 3–5 days to first intro
- 14–21 days to offer
- 100% refund guarantee
TTR-reported benchmarks
TTR-reported proof
- 14–21 days to offer vs industry 60–90 days
- Every candidate passes the Distributed Systems Filter
TTR-reported figures. Timelines are firm benchmarks, not a promise for every mandate. Guarantee terms are confirmed in the intro call.
What you get
What you get when you hire AI infrastructure engineers with TTR
- 01
A scoped mandate first
Start with a working session with Emily Landon on why the role exists now, what it owns and who decides.
- 02
First intros in 3–5 days
TTR-reported benchmark from a scoped mandate to the first candidate introductions.
- 03
Vetting on two layers
Candidates are judged on evidence of judgement in the function and on operating ownership. Every candidate passes the Distributed Systems Filter.
- 04
An offer in 14–21 days
TTR benchmark, against 60–90 days industry-wide. Scope, seniority and decision speed on your side move it.
- 05
Hires that hold
TTR reports 98% long-term retention. The search is built for a durable hire, not only a closed one.
- 06
100% refund guarantee
TTR reports a 100% refund guarantee. The specific terms are confirmed on the intro call with Emily Landon.
Roles
Roles TTR searches for
- Inference and serving engineers
- GPU cluster and orchestration engineers
- MLOps and evaluation pipeline engineers
- Applied engineers who have owned production serving end to end
- Head of AI Infrastructure when serving cost or reliability is a company-level risk and no one owns it
Vetting
How TTR vets AI infrastructure engineers
Two layers of the hire
Engineering layer: durability and security. Separate research from production. If the pain is latency, spend or outages, hire the person who keeps models serving. Write the request path, queue, GPU scheduling and how a silent quality drop is noticed. Ask for a past failure and the fix, including what they got wrong first.
Operational layer: security reviews, data handling, vendor risk and weekly shipping cadence. Name one decision owner and clarify how the role relates to existing platform engineers. Do not blend build, scale and govern.
Distributed Systems Filter
Every candidate passes the Distributed Systems Filter
Every candidate TTR introduces passes the Distributed Systems Filter, across engineering and operational roles. Ask Emily Landon how the filter applies to your mandate and what evidence will be used to assess candidates.
- Applies to every candidate TTR introduces
- Used across operational roles as well as engineering
- Ask Emily Landon what evidence will be used for your mandate
Speed
How fast can you hire AI infrastructure engineers?
Firm benchmarkTTR reports 3–5 days to initial candidate introductions and 14–21 days to offer, against 60–90 days industry-wide. These are firm benchmarks, not a promise for every mandate; scope, seniority and decision speed on your side all move them.
- 3–5 days to first intro (TTR benchmark)
- 14–21 days to offer (TTR benchmark)
- 60–90 days industry range, shown for comparison
TTR: 14–21 days to offer
Industry: 60–90 days
TTR-reported benchmark, not a promise for every mandate.
Guarantee
Backed by a 100% refund guarantee
TTR reports a 100% refund guarantee. The specific terms are confirmed on the intro call with Emily Landon, so you know them before the search starts.
- 100% refund guarantee (TTR-reported)
- 98% long-term retention (TTR-reported)
- 400+ placements (TTR-reported)
Process
How the search works
- 01
Scope the mandate
Scope whether the role builds, scales or governs the inference stack, the boundary and who decides.
- 02
Calibrate the bar
Calibrate on a real production failure and the fix, including what the candidate got wrong first. Every candidate passes the Distributed Systems Filter; confirm the assessment evidence with Emily Landon.
- 03
Introduce candidates
Introduce candidates with context. TTR reports 3–5 days to first intro as a benchmark, not a promise for every mandate.
First intros: 3–5 days (TTR benchmark) - 04
Close and settle in
Close with offer support. TTR reports 14–21 days to offer against 60–90 days industry-wide as benchmarks, not a promise for every mandate. Match the start date to the next reliability or cost milestone.
Offer: 14–21 days (TTR benchmark)
Compared
TTR against the industry range
| Measure | Industry range | TTR |
|---|---|---|
| Time to offer | 60–90 days | 14–21 days |
| Time to first intro | Not compared | 3–5 days |
| Vetting | Not compared | Distributed Systems Filter |
| Long-term retention | Not compared | 98% |
TTR-reported figures. The 60–90 day industry range is the only external reference shown. Every TTR candidate passes the Distributed Systems Filter.
Who it's for
Who this search is for
This search is for founders hiring an AI infrastructure engineer rather than a generic ML title, for inference serving, GPU orchestration, evaluation and monitoring, or the person who governs that stack.
Settle whether the role builds, scales or governs, what production-failure evidence you will accept, who decides, and how the start sequences against the next reliability or cost milestone.
Recently funded
Venture-backed or early-stage companies hiring after a raise.
Under 50 employees preferred
The best-fit company profile.
Under 200 employees maximum
Past that size, a different search model may fit better.
Built for founders
TTR runs specialist search for founders of venture-backed and early-stage companies.
Board-level mandates at established companies may suit a retained firm better. The retained search comparison sets out where that line falls.
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See the searchFAQ
Questions about hiring AI infrastructure engineers
What is the difference between MLOps and AI infrastructure?
MLOps focuses on the lifecycle of models. AI infrastructure focuses on the serving and compute substrate. Many roles blend them, so scope matters.
Do we need a Head of AI Infrastructure early?
When serving cost or reliability is a company-level risk and no one owns it, yes. Otherwise a senior engineer may be enough.
How do you assess GPU orchestration experience?
Ask about scheduling decisions, utilisation problems and incidents they have personally handled, including what they got wrong first.
Should we hire a research scientist for a production serving role?
Do not default a production seat to a research scientist. If the pain is latency, spend or outages, hire the person who has kept models serving in production.
How long does the search take?
TTR reports 3–5 days to first intro and 14–21 days to offer, against 60–90 days industry-wide. These are firm benchmarks, not a promise for every mandate.
What if the hire does not work out?
TTR reports a 100% refund guarantee. Terms are confirmed with Emily Landon in the intro call.
Next step
Put the mandate in front of Emily
Bring the role, even if the scope is unsettled. The intro call is where we pressure-test it and confirm guarantee terms.