Des Moines, IA
ML engineering recruiting for production model teams. Crosscheck recruits AI/ML & LLM Engineering candidates for contract, contract-to-hire, and permanent roles tied to Des Moines.
What We Place
Representative roles
Platforms and technologies
Our Approach
This editorial hiring guide starts with sourced Des Moines business context. Des Moines' 2025 official statement lists major regional employers across financial services and insurance, health care and education, distribution, agricultural equipment, seed manufacturing, and other manufacturing. The list supplies current operating contexts without asserting open requisitions. 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 Des Moines 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
Tie each must-have requirement to a task, system, risk, or deadline. Remove industry preferences that do not change how the person will perform the job. This is planning guidance, not measured local demand.
Editorial industry scenario
A finance-facing brief should identify the transaction, reporting, audit, privacy, and availability requirements attached to the role. 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
1,240
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 3.074 jobs per one thousand across the metro workforce.
Employment concentration
1.82 location quotient
Des Moines-West Des Moines, IA reports an above-national employment concentration for this proxy occupation. Confirm current availability through the active search.
Annual wage reference
$65,480 to $150,190
The metro median is 14% below the national Data Scientists median. Do not use the gap to discount niche platform or domain experience. BLS reports a $103,030 median for the proxy occupation in Des Moines-West Des Moines, IA.
Hiring brief scenarios
These scenarios connect location context to role responsibilities. Use them as prompts to verify with the employer, not as measures of Des Moines demand, clients, or candidate supply.
Sourced financial services and insurance context
A September 2025 City of Des Moines official statement lists financial-services and insurance organizations among the metropolitan area's larger employers. 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. Financial and insurance systems can connect accounts, policies, premiums, claims, payments, identity, risk rules, approvals, reconciliations, statements, reporting, and audit evidence.
Evidence to request: Review a feature or training pipeline and ask the candidate to trace one record from source through validation, training, and evaluation. Name the account, product, policy, transaction, or claim, source ledger, money movement, identity model, control owner, reconciliation, close or reporting date, and support obligation.
Sourced health care and education context
The same Des Moines document lists hospitals, clinics, a health-benefits firm, and public education among the metropolitan area's larger employers. 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 and education environments may join patient or student records, appointments, benefits, billing, learning systems, workforce data, access controls, retention, and reporting.
Evidence to request: Use a production scenario with throughput, latency, and reliability limits. Require an API or batch design plus a test plan. Set the care, benefits, teaching, or administrative workflow, authoritative record, protected data class, access reviewer, integration, reporting rule, retention requirement, and acceptance owner.
Sourced distribution and agricultural manufacturing context
Des Moines' 2025 employer table also includes distribution and data-center work, farm equipment, seed manufacturing, package shipping, agricultural tires, and other manufacturing. 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 cross product engineering, seeds or materials, production, quality, equipment, warehouses, carriers, inventory, status events, delivery, and financial settlement.
Evidence to request: Ask for a model degradation example and the evidence used to separate data change, code change, and user-behavior change. Choose the product or shipment, then trace its source specification, production or receipt, quality gate, serial or lot, inventory event, carrier handoff, delivery exception, settlement, and owner.
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 model evaluation, sensitive-data handling, explainability, and controls around automated decisions. The finance and insurance systems context is an editorial scenario, not a measured claim about Des Moines.
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 model evaluation, sensitive-data handling, explainability, and controls around automated decisions. The finance and insurance systems context is an editorial scenario, not a measured claim about Des Moines.
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 model evaluation, sensitive-data handling, explainability, and controls around automated decisions. The finance and insurance systems context is an editorial scenario, not a measured claim about Des Moines.
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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