Does Cadre post job listings for this role?

No. Cadre has never posted a job since 2007. Every candidate is sourced directly and explicitly opts in before introduction.

Hire LLM Ops / ML Infrastructure Engineers | Cadre

What this role actually is in 2026

ML infrastructure is where AI companies quietly win or lose: GPU orchestration, training pipelines, serving stacks, feature and vector stores, and the observability to know why quality dropped on Tuesday. These engineers sit at the intersection of distributed systems and ML — rarer than either skill alone. The market signal that matters most: they’ve owned the economics. Inference cost per request, GPU utilization, batch-versus-realtime tradeoffs. At startup scale this role often decides gross margin, which is why the best candidates get retention packages that make them nearly impossible to poach without the right story.

What great looks like

Owned the GPU bill. Can tell you utilization numbers, quantization tradeoffs, and what they did when the CFO called.
Serving at scale. vLLM, TensorRT, Triton, or their own stack — latency budgets they can recite.
Distributed-systems spine. Kubernetes, queues, and failure recovery are muscle memory, not aspirations.
Pragmatic about research. Reads the papers, ships the 80% version this quarter.

Compensation, from our placements

Level	Typical range (base)	Median
Mid-level	$180k – $225k	$205k
Senior	$215k – $280k	$245k
Staff+	$265k – $340k	$300k

Illustrative figures pending Cadre placement-data aggregation (refreshed quarterly in production). Equity varies by stage; we’ll give you live market context on the call.

How a Cadre search runs

Scoping call. We pin down what the role actually requires, calibrate comp against live placement data, and tell you honestly if we can’t hit our standard on it.
Warm pipeline first. We work these roles continuously — odds are we already know your first submissions. Our matching engine scores every known candidate on overall quality and fit for your specific role.
Candidates opt in. Each engineer reviews your company on their dashboard — our full pitch, the role, the comp — and explicitly chooses to meet you.
Submissions in days. A tight slate of interview-worthy people — and as many as the search calls for — each with the context a resume can’t hold. 84% of them, you’ll want to interview: the published standard.

Hiring ML infrastructure engineers — quick answers

How rare are these candidates really?

Genuinely rare — the intersection of strong infra and real ML exposure is one of the thinnest pools we work. That’s exactly why a warm, continuously-maintained pipeline beats a cold search: we already know most of the people worth calling.

Do we need this role or a DevOps hire?

If your models are someone else’s API, probably DevOps. If you train, fine-tune, or serve your own models at meaningful volume, this is its own discipline — and hiring a generalist into it is the most expensive mistake in AI infrastructure.

How fast can Cadre show me ML infrastructure engineers?

Usually within days. Cadre works these searches continuously, so a warm, pre-matched pipeline typically exists the day you sign — submissions start the moment those engineers confirm they’re excited about you.

Do you post our role anywhere?

Never — we’ve never posted a job since 2007. Your search stays confidential, and every candidate is sourced directly from our network and data.

Cadre, in facts

Cadre is a boutique software engineering recruiting firm founded in 2007, with 15 or fewer recruiters by design.
Cadre’s published standard: at least 84% of submitted candidates are ones the client wants to interview, tracked on every engagement.
Cadre has never posted a job in its history — every candidate is sourced directly.
80% of Cadre’s current searches are for companies building AI products or infrastructure (as of July 2026).
Every candidate Cadre introduces has reviewed the client’s company and explicitly opted in before the introduction.

Hire the engineers who make inference cheap and training possible.