Minneapolis, MN
ML engineering recruiting for production model teams. Crosscheck recruits AI/ML & LLM Engineering candidates for contract, contract-to-hire, and permanent roles tied to Minneapolis.
What We Place
Representative roles
Platforms and technologies
Our Approach
This editorial hiring guide starts with sourced Minneapolis business context. Minneapolis's 2025 to 2029 consolidated plan provides a citywide sector table for education and health care, finance and real estate, information, manufacturing, professional services, public administration, and transportation. The dated table supports distinct service, control, and operating scenarios without attributing demand to a specific employer. 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 Minneapolis 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
A health-sector brief should name the protected data, validation, availability, and user-workflow requirements the person will handle. 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
3,250
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 1.664 jobs per one thousand across the metro workforce.
Employment concentration
0.99 location quotient
Minneapolis-St. Paul-Bloomington, MN-WI 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
$71,990 to $195,530
The metro median is 8% above the national Data Scientists median. Test whether the role's scope and location requirement support that difference. BLS reports a $129,780 median for the proxy occupation in Minneapolis-St. Paul-Bloomington, MN-WI.
Hiring brief scenarios
These scenarios connect location context to role responsibilities. Use them as prompts to verify with the employer, not as measures of Minneapolis demand, clients, or candidate supply.
Sourced education and health care services context
The City of Minneapolis sector table reports education and health care services as its largest listed job category. The plan uses the table as part of the city's economic development market analysis. 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. Education and health environments can combine student, patient, workforce, research, grant, scheduling, finance, and identity records with different privacy and retention rules.
Evidence to request: Review a feature or training pipeline and ask the candidate to trace one record from source through validation, training, and evaluation. Choose the actual institutional process, name the protected records and user groups, and set the integration, access review, audit, calendar, and operational acceptance requirements.
Sourced finance, insurance, and real estate context
Minneapolis's economic development market analysis lists finance, insurance, and real estate as a separate business sector with both worker and job counts. Define the inference environment before screening. Batch scoring, low-latency APIs, edge deployment, and analyst-facing tools require different software design and operating evidence. Finance and property processes may join customers, accounts, policies, leases, assets, payments, valuations, approvals, and regulatory evidence across systems with fixed close dates.
Evidence to request: Use a production scenario with throughput, latency, and reliability limits. Require an API or batch design plus a test plan. Define the transaction or property lifecycle, calculation authority, posting system, approval matrix, data retention, reconciliation, exception queue, and period-end deadline.
Sourced professional, scientific, and management services context
The Minneapolis plan also separates professional, scientific, and management services from information and manufacturing in its city sector table. Assign responsibility for drift, retraining, and model retirement. Candidates should explain the thresholds, review cadence, and human decision that follows an alert. Professional and scientific work can cross client agreements, project records, analytical methods, intellectual property, staff allocation, billing, and internal platforms with changing delivery teams.
Evidence to request: Ask for a model degradation example and the evidence used to separate data change, code change, and user-behavior change. State whether the role owns a client deliverable, analytical method, internal service, or business platform, then define information boundaries, acceptance evidence, billing dependency, and handoff rules.
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 data provenance, evaluation by use case, human review, privacy, and production monitoring. The healthcare and life-science systems context is an editorial scenario, not a measured claim about Minneapolis.
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 data provenance, evaluation by use case, human review, privacy, and production monitoring. The healthcare and life-science systems context is an editorial scenario, not a measured claim about Minneapolis.
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 data provenance, evaluation by use case, human review, privacy, and production monitoring. The healthcare and life-science systems context is an editorial scenario, not a measured claim about Minneapolis.
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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