AI systems · ML inference · GPU orchestration · MLOps
Hire the engineers who keep AI systems running in production
Search for ML inference, GPU orchestration and MLOps engineers: the infrastructure layer that decides whether a model is a demo or a product.
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 and ML 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
- ML infrastructure and inference engineers
- GPU cluster and orchestration engineers
- MLOps and evaluation pipeline engineers
- Applied ML engineers with production ownership
- Data platform engineers
- Head of AI Infrastructure
- VP Engineering for AI-native companies
Vetting
How TTR vets AI and ML engineers
Two layers of the hire
Engineering layer: durability and security. Serving paths fail in specific ways: queue collapse, memory pressure, silent quality regression. We look for people who have operated these systems and can describe the failure and the fix.
Operational layer: velocity and compliance. Model-driven products move fast and touch sensitive data. The hire needs to ship quickly while respecting data handling, vendor risk and customer security review.
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 and ML 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
Define whether you need to build serving infrastructure, scale it, or govern it, since these are different people.
- 02
Calibrate the bar
Agree the signal: production systems owned, scale handled, trade-offs between latency, throughput and cost explained.
- 03
Introduce candidates
Introductions with context on the specific systems each candidate has run.
First intros: 3–5 days (TTR benchmark) - 04
Close and settle in
Offer support and follow-through after the start date.
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 whose product depends on serving models reliably and affordably, and who have discovered that research talent and production talent are different searches.
It suits early teams moving from API calls to owning their own inference stack, and companies building AI-native products with real latency and cost constraints.
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.
Related searches
Related roles TTR hires for
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See the searchFAQ
Questions about hiring AI and ML engineers
Do you search for research scientists?
The focus is AI systems and infrastructure engineering: inference, orchestration, MLOps and the leaders who run them. Research-heavy mandates can be discussed in the intro call.
What separates an MLOps hire from a platform hire?
MLOps concerns the lifecycle of models: training, evaluation, deployment, monitoring. Platform engineering concerns the general substrate. Many roles blend the two, which is why scope comes first.
How quickly can we see candidates?
TTR reports 3–5 days to initial introductions and 14–21 days to offer as firm benchmarks, not a promise for every mandate.
Is there a guarantee?
TTR reports a 100% refund guarantee. Terms are confirmed 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.