San Jose, CA
ML engineering recruiting for production model teams. 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. 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 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 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
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. 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. 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: Review a feature or training pipeline and ask the candidate to trace one record from source through validation, training, and evaluation. 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 the inference environment before screening. Batch scoring, low-latency APIs, edge deployment, and analyst-facing tools require different software design and operating evidence. Semiconductor and manufacturing programs may connect product definitions, equipment, process recipes, production schedules, quality results, suppliers, inventory, maintenance, and cost records.
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 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. Assign responsibility for drift, retraining, and model retirement. Candidates should explain the thresholds, review cadence, and human decision that follows an alert. These facilities can combine power, cooling, networks, physical security, capacity, asset maintenance, environmental controls, backup systems, and tenant or workload commitments.
Evidence to request: Ask for a model degradation example and the evidence used to separate data change, code change, and user-behavior change. 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. 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 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. 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 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. 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 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 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.
Local hiring 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 San Jose contexts below turn that scope into intake and screening decisions. They do not measure current vacancies, candidate supply, or Crosscheck client activity. Set data ownership, evaluation criteria, service integration, latency, cost, monitoring, retraining, and rollback duties. Ask candidates to diagnose a failure that crosses model, data, and application boundaries.
Sources and methodology
Original Crosscheck visual
Each lane connects sourced regional context to a role-specific screening decision. The sources do not measure current candidate supply or Crosscheck client demand.
Review a feature or training pipeline and ask the candidate to trace one record from source through validation, training, and evaluation. Define the user decision, model boundary, source data, evaluation set, deployment path, access control, cost target, failure response, and approving product owner.
Use a production scenario with throughput, latency, and reliability limits. Require an API or batch design plus a test plan. Set the design or plant boundary, product revision, process control, equipment interface, traceability unit, quality release, supplier handoff, change window, and support owner.
Ask for a model degradation example and the evidence used to separate data change, code change, and user-behavior change. Name the facility and workload boundary, capacity unit, power and cooling dependencies, availability target, access model, maintenance path, recovery test, and change authority.
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