ML Engineer
Designs, trains, and deploys machine learning models across structured and unstructured data problems.
AI/ML & LLM Engineering
ML Engineer staffing across the US and Canada
ML engineering recruiting for production model teams. Choose a market below for local hiring context and the screening priorities Crosscheck uses for this role.
Screen ML engineers on modeling fundamentals and production tradeoffs
Match experience to the ML stack, data, and problem defined in the brief
target a first candidate slate within 48 hours for qualified exclusive searches in our core disciplines after a completed intake
Permanent placements include a 90-day replacement guarantee, subject to the signed agreement.
Role-specific recruiting
What a focused ML Engineer search covers
A successful search starts with the outcomes this person must own, the environment they will inherit, and the evidence that separates production experience from keyword familiarity. Crosscheck aligns those requirements during intake, then screens a specialist network against the agreed role, compensation, location, and interview process.
Screen ML engineers on modeling fundamentals and production tradeoffs
Match experience to the ML stack, data, and problem defined in the brief
target a first candidate slate within 48 hours for qualified exclusive searches in our core disciplines after a completed intake
Permanent placements include a 90-day replacement guarantee, subject to the signed agreement.
Role coverage
Related ML Engineer profiles
The exact title varies by team structure and project stage. These are the adjacent profiles commonly considered during intake and technical screening.
Senior ML Engineer
Leads model development, mentors junior engineers, and owns the ML lifecycle from research to production.
Staff / Principal ML Engineer
Cross-team technical leadership, architecture decisions, and research direction for mature ML platforms.
ML Research Engineer
Implements novel architectures, reproduces papers, and translates research breakthroughs into production advantage.
Applied Scientist
Statistical modeling, experimentation design, and applied ML research tied to real product outcomes.
Computer Vision Engineer
Image and video model development, object detection, segmentation, VLMs, and diffusion pipelines.
Technical and functional screening scope
The intake identifies which areas are essential on day one and which can be adjacent experience. Screening then focuses on decisions made, systems shipped, constraints handled, and measurable outcomes.
Frameworks
PyTorch · TensorFlow · JAX · scikit-learn · XGBoost / LightGBM
LLM & Transformers
HuggingFace Transformers · HuggingFace PEFT · Accelerate · Diffusers · TRL
ML Platform & Ops
MLflow · Weights & Biases · SageMaker · Vertex AI · AzureML
Data & Pipelines
Spark / PySpark · Databricks · Pandas / Polars · Ray · Airflow
Infrastructure
Docker / Kubernetes · CUDA / GPU clusters · AWS / GCP / Azure · Terraform · NVIDIA Triton
Specialized
Computer Vision (OpenCV, detectron2) · NLP (spaCy, NLTK) · RL (Gymnasium, RLlib) · Time Series (Prophet, NeuralForecast) · Recommender Systems
Interview calibration
How to evaluate ML Engineer experience
The search team uses the completed brief to separate adjacent familiarity from work the candidate owned. Each interviewer should use the same scenario, record the evidence provided, and score the answer against the responsibilities agreed during intake.
ML Engineer: Frameworks
Designs, trains, and deploys machine learning models across structured and unstructured data problems.
Interview prompt: Ask for one decision involving PyTorch, TensorFlow, JAX. Record the constraint, what the candidate owned, and the evidence used to evaluate the result.
Brief alignment: Screen ML engineers on modeling fundamentals and production tradeoffs
Senior ML Engineer: LLM & Transformers
Leads model development, mentors junior engineers, and owns the ML lifecycle from research to production.
Interview prompt: Ask for one decision involving HuggingFace Transformers, HuggingFace PEFT, Accelerate. Record the constraint, what the candidate owned, and the evidence used to evaluate the result.
Brief alignment: Match experience to the ML stack, data, and problem defined in the brief
Staff / Principal ML Engineer: ML Platform & Ops
Cross-team technical leadership, architecture decisions, and research direction for mature ML platforms.
Interview prompt: Ask for one decision involving MLflow, Weights & Biases, SageMaker. Record the constraint, what the candidate owned, and the evidence used to evaluate the result.
Brief alignment: target a first candidate slate within 48 hours for qualified exclusive searches in our core disciplines after a completed intake
Market directory
Choose where you need to hire.
Use a quick link or choose a state or province. Every market opens a city-specific ML Engineer hiring guide.
Quick market links
United States by state
Open states with multiple cities
Alabama (AL)BirminghamArkansas (AR)FayettevilleConnecticut (CT)HartfordDistrict of Columbia (DC)WashingtonGeorgia (GA)AtlantaIdaho (ID)BoiseIllinois (IL)ChicagoIndiana (IN)IndianapolisIowa (IA)Des MoinesKansas (KS)WichitaKentucky (KY)LouisvilleMaryland (MD)BaltimoreMassachusetts (MA)BostonMinnesota (MN)MinneapolisNebraska (NE)OmahaNevada (NV)Las VegasNew Mexico (NM)AlbuquerqueOregon (OR)PortlandRhode Island (RI)ProvidenceUtah (UT)Salt Lake CityVirginia (VA)Richmond
California (CA)5 cities
Colorado (CO)2 cities
Florida (FL)4 cities
Louisiana (LA)2 cities
Michigan (MI)2 cities
Missouri (MO)2 cities
North Carolina (NC)4 cities
Ohio (OH)3 cities
Oklahoma (OK)2 cities
Pennsylvania (PA)2 cities
South Carolina (SC)2 cities
Tennessee (TN)4 cities
Texas (TX)5 cities
Canada by province
Choose a province to open its markets
Need a wider or fully remote search?
The market pages are planning guides, not claims of a physical office in every city. Crosscheck recruits across the United States and Canada from Denver. For qualified exclusive searches in our core disciplines, Crosscheck targets a first candidate slate within 48 hours after a completed intake.
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