Houston, TX
ML engineering recruiting for production model teams. Crosscheck recruits AI/ML & LLM Engineering candidates for contract, contract-to-hire, and permanent roles tied to Houston.
What We Place
Representative roles
Platforms and technologies
Our Approach
This editorial hiring guide starts with sourced Houston business context. City of Houston economic-development materials identify energy, advanced manufacturing, life science, aerospace, logistics, and international trade. Those sectors create different system, data, asset, and support requirements for a technical search. 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 Houston 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 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
4,060
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 1.235 jobs per one thousand across the metro workforce.
Employment concentration
0.73 location quotient
Houston-Pasadena-The Woodlands, TX reports a below-national employment concentration for this proxy occupation. Decide which requirements justify a wider regional or remote search.
Annual wage reference
$62,980 to $167,230
The metro median is 11% below the national Data Scientists median. Do not use the gap to discount niche platform or domain experience. BLS reports a $106,750 median for the proxy occupation in Houston-Pasadena-The Woodlands, TX.
Hiring brief scenarios
These scenarios connect location context to role responsibilities. Use them as prompts to verify with the employer, not as measures of Houston demand, clients, or candidate supply.
Sourced energy and industrial operations context
The City of Houston's economic-development program lists energy, petroleum and chemical products, advanced technology, and manufacturing among its industry clusters. 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. Industrial systems can connect plants, sites, equipment, products, service teams, production records, maintenance events, and finance systems with different update cycles.
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 asset or product lifecycle, locations, field users, source systems, outage limits, exception path, and operational sign-off attached to the role.
Sourced life science and aerospace context
Houston's Mayor's Office of Trade and International Affairs identifies the city as a leader in life science, manufacturing, logistics, and aerospace. Define the inference environment before screening. Batch scoring, low-latency APIs, edge deployment, and analyst-facing tools require different software design and operating evidence. Research and engineering programs may combine controlled data, quality evidence, specialized facilities, assets, supplier records, and contract requirements across several systems.
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 program stage, data classes, validation or quality records, facility boundary, connected partners, and approval evidence required before release.
Sourced international trade and logistics context
The same city office leads Houston's trade development and describes the city as an international business center with strong logistics activity. Assign responsibility for drift, retraining, and model retirement. Candidates should explain the thresholds, review cadence, and human decision that follows an alert. Cross-border operations can involve partner records, currencies, languages, shipment events, trade documents, tax handoffs, and duplicate data from several sources.
Evidence to request: Ask for a model degradation example and the evidence used to separate data change, code change, and user-behavior change. Map the partner and shipment lifecycle, systems of record, regional ownership, matching rules, integration recovery, and financial reconciliation.
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 Houston.
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 asset data, forecast evaluation, field constraints, monitoring, and operator review. The energy systems and field operations context is an editorial scenario, not a measured claim about Houston.
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 Houston.
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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