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 Computer Vision / NLP Engineers | Cadre — 84% Interview-Worthy Standard

What this role actually is in 2026

Foundation models absorbed a lot of classic CV and NLP, but the companies building in robotics, autonomy, medical imaging, document intelligence, and multimodal products still need specialists who understand the layer beneath the API: data pipelines for annotation, model selection and fine-tuning, latency-constrained deployment on edge devices, and the long tail of domain-specific failure. The modern profile blends classical foundations (geometry, signal processing, linguistics-adjacent intuition) with fluency in the transformer era. Purely academic profiles abound; the scarce ones have shipped perception or language systems that ran in production against messy, adversarial, real-world data.

What great looks like

Production data scars. They’ve handled distribution shift, label noise, and the demo-to-deployment gap.
Edge and latency literacy. Model compression, on-device constraints, and streaming inference where it matters.
Fine-tuning judgment. Knows when to fine-tune, when to prompt, and when to collect better data instead.
Domain curiosity. The best CV/NLP engineers become part-experts in your domain — radiology, driving, documents.

Compensation, from our placements

Level	Typical range (base)	Median
Mid-level	$175k – $215k	$195k
Senior	$205k – $265k	$235k
Staff+	$255k – $330k	$285k

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 CV and NLP engineers — quick answers

Has GenAI made these specialists obsolete?

No — it moved the bar. Companies building on cameras, sensors, documents, or domain language still need engineers who own the full pipeline, and the multimodal wave has made real perception experience more valuable, not less.

Can you assess this technically?

We scope with your technical leadership, and our screens probe for shipped systems and failure stories rather than benchmark trivia — then your loop makes the deep technical call. 84% of who we send, you’ll want in that loop.

How fast can Cadre show me CV and NLP 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 perception and language engineers who work beyond the API call.