San Jose, CA
MLOps engineers who bridge data science and production. Crosscheck recruits AI/ML & LLM Engineering candidates for contract, contract-to-hire, and permanent roles tied to San Jose.
What We Place
Representative roles
Platforms and technologies
Our Approach
This editorial hiring guide starts with sourced San Jose business context. San Jose's current economic-development work separates artificial intelligence, semiconductor and advanced manufacturing, and large energy-use infrastructure. These settings require different technical evidence for software products, physical production, and high-availability facilities. An MLOps Engineer owns the controls that move models from experiments into supported services. The search brief should name the training environment, registry, deployment targets, approval path, observability stack, and teams that share the platform.
Source MLOps engineers with production ownership of model pipelines and serving infrastructure
Vet on model serving, experiment tracking, feature stores, and retraining workflows
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
Separate platform construction from day-to-day model operations. Some teams need reusable pipelines and infrastructure; others need release governance, incident response, cost control, or migration from manually operated notebooks. The three sourced San Jose 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
Set the system boundary, decision rights, work model, and interview schedule before sourcing. Candidates can then compare the role on concrete responsibilities. This is planning guidance, not measured local demand.
Editorial industry scenario
A cross-industry brief should start with the systems, users, risks, and outcomes behind the job title. 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 MLOps 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
6,060
BLS publishes a sizable metro employment estimate for the proxy occupation. The intake still needs to isolate the platform, delivery stage, and ownership required here. The estimate equals 5.339 jobs per one thousand across the metro workforce.
Employment concentration
3.16 location quotient
San Jose-Sunnyvale-Santa Clara, CA reports more than twice the national employment concentration for this proxy occupation. Treat that as occupational context, not proof of available candidates.
Annual wage reference
$109,740 to $282,840
The metro median is 54% above the national Data Scientists median. Test whether the role's scope and location requirement support that difference. BLS reports a $185,080 median for the proxy occupation in San Jose-Sunnyvale-Santa Clara, CA.
Hiring brief scenarios
These scenarios connect location context to role responsibilities. Use them as prompts to verify with the employer, not as measures of San Jose demand, clients, or candidate supply.
Sourced artificial intelligence and software context
The City of San Jose identifies artificial intelligence as a priority growth sector and includes AI training and job-matching programs in its fiscal year 2025 to 2026 economic plan. Require reproducible training and lineage for the local data setting. The candidate should account for code, configuration, data versions, model artifacts, and approval records without copying sensitive data into uncontrolled tools. AI product work can join source data, models, application code, evaluation, user feedback, cost controls, access rules, and production support under separate owners.
Evidence to request: Ask for a pipeline diagram or repository structure that shows lineage, repeatable environments, and artifact retention. Define the user decision, model boundary, source data, evaluation set, deployment path, access control, cost target, failure response, and approving product owner.
Sourced semiconductors and advanced manufacturing context
A July 2025 City of San Jose economic-development release names advanced manufacturing and semiconductors among the industries the city plans to attract, retain, and grow. Define promotion between development, test, and production. Ask who approves a model, which automated checks block release, and how the team handles a failed deployment. Semiconductor and manufacturing programs may connect product definitions, equipment, process recipes, production schedules, quality results, suppliers, inventory, maintenance, and cost records.
Evidence to request: Review a release-control example with test gates, registry state, deployment strategy, and rollback steps. Set the design or plant boundary, product revision, process control, equipment interface, traceability unit, quality release, supplier handoff, change window, and support owner.
Sourced data centers and energy infrastructure context
San Jose's July 2026 large energy-use project page distinguishes data centers from research laboratories, advanced manufacturing sites, electric-vehicle charging hubs, and other power-intensive facilities. Set operating ownership for model services and training jobs. Include capacity, cost, feature freshness, prediction quality, and rollback signals in the interview scenario. These facilities can combine power, cooling, networks, physical security, capacity, asset maintenance, environmental controls, backup systems, and tenant or workload commitments.
Evidence to request: Use an incident involving stale features, failed training, or degraded predictions and score the candidate's diagnosis path. Name the facility and workload boundary, capacity unit, power and cooling dependencies, availability target, access model, maintenance path, recovery test, and change authority.
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. MLOps Engineer: Kubeflow
Choose a Kubeflow decision from your work as MLOps Engineer. Which constraint changed the design, and what evidence supported the result?
Use the answer to assess the model or application, evaluation method, input data, production limits, and owner after launch. The cross-industry technical work context is an editorial scenario, not a measured claim about San Jose.
2. ML Platform Engineer: MLflow
Describe project work you completed as ML Platform Engineer involving MLflow that did not follow the original plan. What did you own, and how did you correct it?
Use the answer to assess the model or application, evaluation method, input data, production limits, and owner after launch. The cross-industry technical work context is an editorial scenario, not a measured claim about San Jose.
3. AI Infrastructure Engineer: Metaflow
For a Metaflow 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 the model or application, evaluation method, input data, production limits, and owner after launch. The cross-industry technical work context is an editorial scenario, not a measured claim about San Jose.
Need the full MLOps 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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