St. Louis, MO
ML engineering recruiting for production model teams. Crosscheck recruits AI/ML & LLM Engineering candidates for contract, contract-to-hire, and permanent roles tied to St. Louis.
What We Place
Representative roles
Platforms and technologies
Our Approach
This editorial hiring guide starts with sourced St. Louis business context. St. Louis Development Corporation identifies geospatial technology, health care innovation, advanced manufacturing, and agricultural technology as drivers of the city economy. Those sectors give hiring teams separate data, security, production, and supply-chain settings for technical searches. A Machine Learning Engineer search needs a defined prediction task, training data owner, deployment path, and measure of useful performance. The same title can describe notebook research, feature engineering, backend development, or ownership of an inference service.
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.
Start the search
Include the business context, systems, delivery phase, work model, compensation, and interview timeline. A Crosscheck search lead will use that context to calibrate the role before sourcing begins.
Local Market Brief
Write the brief around the model lifecycle. Include label creation, feature pipelines, experiment tracking, service integration, monitoring, and retraining duties that belong to this hire. Separate those duties from work owned by data, platform, or research teams. The three sourced St. Louis contexts below turn that scope into intake and screening decisions. They do not measure current vacancies, candidate supply, or Crosscheck client activity.
Editorial market scenario
Give candidates the integration, approval, audit, and change-management boundaries during the interview. Ask for comparable decisions from prior work. This is planning guidance, not measured local demand.
Editorial industry scenario
An industrial brief should show how software, data, and infrastructure connect to plants, equipment, schedules, quality, and frontline users. Confirm that this context applies to the employer before using it in the search.
Screening focus
We test for model or application ownership, evaluation discipline, data judgment, and evidence that the candidate has shipped reliable AI systems.
Published labor benchmark
BLS does not publish an occupation matching ML Engineer. Crosscheck uses Data Scientists (15-2051) as the closest published broad benchmark; it is not a count or pay estimate for this exact specialty.
BLS OEWS May 2025, published May 15, 2026
Published metro employment
2,640
BLS publishes a narrower metro employment estimate for the proxy occupation. Screen for adjacent experience that transfers without lowering the production bar. The estimate equals 1.969 jobs per one thousand across the metro workforce.
Employment concentration
1.17 location quotient
St. Louis, MO-IL sits near the national employment concentration for this proxy occupation. Use role evidence and work-model requirements to set the sourcing radius.
Annual wage reference
$59,500 to $158,310
The metro median is 17% below the national Data Scientists median. Do not use the gap to discount niche platform or domain experience. BLS reports a $99,600 median for the proxy occupation in St. Louis, MO-IL.
Hiring brief scenarios
These scenarios connect location context to role responsibilities. Use them as prompts to verify with the employer, not as measures of St. Louis demand, clients, or candidate supply.
Sourced geospatial technology context
A current St. Louis Development Corporation feature names geospatial technology as a driver of the city economy and describes T-REX as an innovation center that works with the National Geospatial-Intelligence Agency. Tie the sector scenario to a concrete outcome and data-generating process. Ask how the engineer would detect label leakage, sampling bias, missing history, and a metric that looks strong but fails the business use case. Geospatial systems can join imagery, sensor feeds, location records, analytical models, map services, access controls, and delivery partners across restricted and public data sets.
Evidence to request: Review a feature or training pipeline and ask the candidate to trace one record from source through validation, training, and evaluation. Define the geographic product, source data, coordinate and accuracy rules, security boundary, model or service interface, update cycle, and approving user.
Sourced health care innovation context
The same St. Louis Development Corporation source identifies health care innovation among the industries that drive the city economy. Define the inference environment before screening. Batch scoring, low-latency APIs, edge deployment, and analyst-facing tools require different software design and operating evidence. Health innovation work may cross research data, patient or member records, laboratories, devices, billing, workforce systems, access review, and formal release controls.
Evidence to request: Use a production scenario with throughput, latency, and reliability limits. Require an API or batch design plus a test plan. Name the care, research, product, or business process, record authority, protected data, integration path, validation evidence, downtime limit, and reviewer.
Sourced advanced manufacturing and agricultural technology context
St. Louis Development Corporation also names advanced manufacturing and agricultural technology among the industries that shape the city economy. Assign responsibility for drift, retraining, and model retirement. Candidates should explain the thresholds, review cadence, and human decision that follows an alert. These operations can connect product formulas or designs, equipment, plants, growers or suppliers, quality, inventory, warehouses, maintenance, traceability, and financial postings.
Evidence to request: Ask for a model degradation example and the evidence used to separate data change, code change, and user-behavior change. Trace one product from design or source through production, quality release, storage, shipment, accounting, exception handling, and change ownership.
Interview scorecard
Ask each candidate the same core questions. Score the evidence, ownership, and judgment in the answer instead of relying on job-title or keyword matches.
1. ML Engineer: PyTorch
Choose a PyTorch decision from your work as ML Engineer. Which constraint changed the design, and what evidence supported the result?
Use the answer to assess sensor or production data, edge constraints, model drift, operator review, and measurable process outcomes. The manufacturing and operational systems context is an editorial scenario, not a measured claim about St. Louis.
2. Senior ML Engineer: TensorFlow
Describe project work you completed as Senior ML Engineer involving TensorFlow that did not follow the original plan. What did you own, and how did you correct it?
Use the answer to assess sensor or production data, edge constraints, model drift, operator review, and measurable process outcomes. The manufacturing and operational systems context is an editorial scenario, not a measured claim about St. Louis.
3. Staff / Principal ML Engineer: JAX
For a JAX system you supported, explain the handoff, operating limits, and measures used after launch. Where did your responsibility begin and end?
Use the answer to assess sensor or production data, edge constraints, model drift, operator review, and measurable process outcomes. The manufacturing and operational systems context is an editorial scenario, not a measured claim about St. Louis.
Need the full ML Engineer evaluation guide?
The role guide covers technical scope, interview questions, and evidence checks once, without repeating the same material on every city page.
FAQ
For qualified exclusive searches in our core disciplines, Crosscheck targets a first candidate slate within 48 hours after a completed intake. Contract placements include a 40-billable-hour replacement guarantee, and permanent placements include a 90-day replacement guarantee, subject to the signed agreement.
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