Senior ML engineers, technically screened and shortlisted in days.
We place ML engineers at AI-native companies across PK/CA/US — research-inclined, production-shipping, and vetted for real model engineering, not just notebook experiments.
The ml engineer market — the honest version.
The ML engineer market is both large and narrow. Large because every Series A is trying to ship AI features. Narrow because the number of candidates who can own a model end-to-end — data, training infrastructure, deployment, monitoring, production incident response — is a small subset of the LinkedIn-self-labeled pool. Our screens separate them. We interview for production ML experience, distinguish research ML from applied ML, and calibrate against the specific part of your stack (inference latency, training throughput, data pipelines, evaluation rigor, or agentic tooling) that matters to you.
What we actually screen for.
Every ml engineer candidate goes through a structured technical screen conducted by an ex-engineer recruiter before you see their profile.
- Production deployment history — what models have they actually shipped, at what scale, and what broke in the first 90 days
- Infrastructure fluency — training pipelines, feature stores, model registries, evaluation loops, and on-call experience
- Framework depth — PyTorch-first candidates vs JAX-first vs TensorFlow legacy; inference stacks (Triton, vLLM, SageMaker, modal)
- Evaluation rigor — do they ship models with harnesses, or vibe-test a benchmark once and call it done
- Collaboration pattern — can they partner with researchers AND data engineers AND product, or do they operate as an island
- Comp expectations calibrated against the market — we will not introduce candidates priced outside your approved band
How we run a ml engineer search.
Days 0–2
Intake + written brief
45-minute call with the hiring manager. We pressure-test the must-haves: classical ML vs LLM engineering, research-leaning vs infra-leaning, IC vs tech-lead behavior.
Days 2–10
Parallel sourcing + technical screen
Our platform sources 200+ contacts across GitHub, Kaggle, research communities, and LinkedIn. Every responder goes through a 30-minute structured technical screen with an ex-engineer recruiter.
Days 10–14
First shortlist
4–6 candidates. Each profile includes a written assessment covering deployment scale, framework depth, and comp expectations — not a resume summary.
Days 14–24
Interviews + offer
We coordinate interview loops, reference checks, and offer structuring. Most placements close inside 24 days from intake.
Real salary bands across our three markets.
- Pakistan (Lahore / Karachi / Islamabad)
PKR 8–22M / year · USD equivalent ~$28K–$80K
Strong senior pool for remote-first international employers
- Canada (Toronto / Waterloo / Vancouver / Montreal)
CAD $140K–$240K + equity
Typical Series B–D range; public tech cos pay above the band
- United States (Austin / Dallas / Miami / Remote)
USD $180K–$320K + equity
Staff-level and principal ML ICs sit at the top of the band
Titles we place under this role type.
- Senior Machine Learning Engineer
- Staff ML Engineer
- Applied ML Engineer
- ML Platform Engineer
- MLOps Engineer
- Research Engineer
- LLM Engineer
ML Engineer hiring — questions we hear.
During intake we ask what the role will actually ship — papers, production models, infrastructure, or applied product features. We then screen candidates against that specific profile. A researcher who has never deployed a model is a bad placement for a Series B shipping user-facing inference; an applied engineer is a bad placement for a frontier lab. Our recruiters separate them explicitly rather than using job-title keywords.
Yes. Since 2024 the bulk of our ML engineering placements have been on LLM infrastructure, agentic systems, RAG pipelines, and eval engineering. We screen for practical exposure (not just tutorials), prompt engineering depth, and familiarity with inference stacks like vLLM, Triton, Modal, or commercial APIs.
First shortlist in 10–14 days for standard senior roles. Closed offer in 21–35 days. Executive-level ML leadership (Head of ML, VP Research) runs 6–10 weeks to shortlist because the pool is smaller and discretion matters more.
Ready to run this search?
Submit a brief and a senior recruiter will reply within 24 business hours with a proposed timeline, calibrated fee structure, and sample profiles.
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