San Francisco, CA
AI Evaluation Engineer recruiting based on accountable delivery experience. Crosscheck recruits AI, ML & Software Engineering candidates for contract, contract-to-hire, and permanent roles tied to San Francisco.
What We Place
Representative roles
Platforms and technologies
Our Approach
This editorial hiring guide starts with sourced San Francisco business context. San Francisco's economic-development material provides dated evidence for AI investment and identifies the Financial District and Mission Bay as distinct business areas. The city profile supports separate AI, finance, and life-sciences hiring scenarios. A AI Evaluation Engineer search should define the operating boundary before comparing resumes. The brief must distinguish AI Evaluation Engineer, LLM Evaluation Engineer, AI Quality Engineer and connect role-specific scope to the work this person will personally own. Screening centers on evaluation design, test datasets, quality rubrics, failure analysis, regression controls, human review, and release decisions.
Define the systems, delivery stage, operating boundary, and ownership expected from the AI Evaluation Engineer
Screen candidates for evidence of evaluation design, test datasets, quality rubrics, failure analysis, regression controls, human review, and release decisions
Separate direct delivery experience from adjacent product, project, or consulting exposure
Support contract, contract-to-hire, and permanent searches across the US and Canada
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
Record the required decisions, systems, delivery stage, and support duties for AI Evaluation Engineer work. Treat Evaluation Harnesses, Benchmarking, Golden Datasets, Human Review as context for the assignment, not a keyword checklist. Separate that scope from adjacent Model Evaluation Scientist, AI Red Team Engineer, Evaluation Technical Lead responsibilities so each candidate is evaluated against the same completed brief. Build the calibration map from the actual assignment: AI Evaluation Engineer against Evaluation Harnesses and Benchmarking; LLM Evaluation Engineer against Golden Datasets and Human Review; AI Quality Engineer against Regression Testing and Safety Testing; Model Evaluation Scientist against Error Analysis and Quality Rubrics; AI Red Team Engineer against evaluation design and test datasets; Evaluation Technical Lead against quality rubrics and failure analysis. For the delivery handoff, trace the working sequence from Safety Testing to Regression Testing to Human Review to Golden Datasets to Benchmarking to Evaluation Harnesses and name who accepts each boundary. The three sourced San Francisco 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 product-company brief should connect the role to users, release decisions, service measures, and ownership after launch. 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 AI Evaluation 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
10,460
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 4.407 jobs per one thousand across the metro workforce.
Employment concentration
2.61 location quotient
San Francisco-Oakland-Fremont, 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
$99,170 to $272,430
The metro median is 41% above the national Data Scientists median. Test whether the role's scope and location requirement support that difference. BLS reports a $170,110 median for the proxy occupation in San Francisco-Oakland-Fremont, 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 Francisco demand, clients, or candidate supply.
Sourced artificial intelligence context
San Francisco's economic-development page reports that city-based companies attracted $34.3 billion in venture funding in 2023 and attributes more than 20 percent of United States AI job postings to the area for that period. These dated figures describe the wider market, not current openings or Crosscheck activity. Define how Regression Testing, Safety Testing, Error Analysis, Quality Rubrics fit the employer's current environment. Ask which constraints changed the design, what AI Evaluation Engineer owned directly, who approved the decision, and how the result was checked after delivery. AI product teams may change model providers, evaluation methods, and data controls while they move from prototypes to supported services.
Evidence to request: Request a redacted design, configuration, test, runbook, review record, or operating measure that supports the candidate's account of AI Evaluation Engineer ownership. Define the product stage, model boundary, data rights, evaluation owner, and production service level before comparing candidate backgrounds.
Sourced financial district context
The City and County of San Francisco identifies the Financial District and the Market Street transit spine as core downtown business areas. The geography supports a financial or enterprise systems scenario, but it does not identify a specific employer or vacancy. Set the boundary for ownership checkpoints before interviews. A useful account involving evaluation design, test datasets, quality rubrics, failure analysis names the starting condition, alternatives considered, implementation sequence, failure handling, and the operating team that received the work. Enterprise finance work can involve high-value records, role-based access, reporting deadlines, and integrations with older platforms.
Evidence to request: Use a comparable scenario involving and release decisions, AI Evaluation Engineer, LLM Evaluation Engineer, AI Quality Engineer and score assumptions, technical judgment, communication, delivery steps, and the evidence proposed for acceptance. Ask which transactions, users, controls, and downstream reports the role supports and whether office presence follows a stated operating need.
Sourced mission bay and research context
San Francisco's economic-development page identifies Mission Bay as one of the city's growing office and industry clusters. Mission Bay contains research and health institutions, so employers may need technical staff who can work with scientific, clinical, or operational systems. Connect adjacent role boundaries to an employer decision rather than a broad tool list. Require the candidate to explain work with regression controls, human review, and release decisions, AI Evaluation Engineer, including dependencies, controls, measurable evidence, and responsibility when the original plan changed. Research and health data can require validation, controlled access, lineage, and communication with scientists or clinical staff.
Evidence to request: Ask for a problem involving LLM Evaluation Engineer responsibilities. Record the signal, diagnosis, decision, corrective action, handoff, and verification the candidate personally completed. Determine whether domain experience is mandatory and name the validation or data-governance artifact a candidate must explain.
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. AI Evaluation Engineer: Evaluation Harnesses
Choose a Evaluation Harnesses decision from your work as AI Evaluation Engineer. Which constraint changed the design, and what evidence supported the result?
Use the answer to assess evaluation tied to user tasks, latency, cost, monitoring, fallback behavior, and product ownership. The product and software delivery context is an editorial scenario, not a measured claim about San Francisco.
2. LLM Evaluation Engineer: Benchmarking
Describe project work you completed as LLM Evaluation Engineer involving Benchmarking that did not follow the original plan. What did you own, and how did you correct it?
Use the answer to assess evaluation tied to user tasks, latency, cost, monitoring, fallback behavior, and product ownership. The product and software delivery context is an editorial scenario, not a measured claim about San Francisco.
3. AI Quality Engineer: Golden Datasets
For a Golden Datasets 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 evaluation tied to user tasks, latency, cost, monitoring, fallback behavior, and product ownership. The product and software delivery context is an editorial scenario, not a measured claim about San Francisco.
Screened for evaluation design, test datasets, quality rubrics, failure analysis, regression controls, human review, and release decisions, with the boundary set by the employer's systems, delivery stage, and operating model. The evaluation connects Evaluation Harnesses, Benchmarking, Golden Datasets to a concrete hiring responsibility.
Show how Evaluation Harnesses, Benchmarking, Golden Datasets shaped one delivery decision. Which constraint mattered, and what did the candidate own?
Evidence check: Look for an artifact, test, configuration record, or operating measure that supports the account. Compare it with work such as technical product and platform teams.
Screened for evaluation design, test datasets, quality rubrics, failure analysis, regression controls, human review, and release decisions, with the boundary set by the employer's systems, delivery stage, and operating model. The evaluation connects Human Review, Regression Testing, Safety Testing to a concrete hiring responsibility.
Where did LLM Evaluation Engineer work involving Human Review, Regression Testing, Safety Testing fail or change direction? What evidence prompted the correction?
Evidence check: A useful answer names the failure signal, the candidate's decision, and the result. Certification alone does not establish project ownership.
Screened for evaluation design, test datasets, quality rubrics, failure analysis, regression controls, human review, and release decisions, with the boundary set by the employer's systems, delivery stage, and operating model. The evaluation connects Error Analysis, Quality Rubrics, evaluation design to a concrete hiring responsibility.
Explain the handoff and operating boundary for a project using Error Analysis, Quality Rubrics, evaluation design. Who approved changes, monitored results, and supported the system?
Evidence check: Request documentation, controls, or production measures that distinguish direct ownership from observation or team-level credit.
Screened for evaluation design, test datasets, quality rubrics, failure analysis, regression controls, human review, and release decisions, with the boundary set by the employer's systems, delivery stage, and operating model. The evaluation connects test datasets, quality rubrics, failure analysis to a concrete hiring responsibility.
Which tradeoff would change the design of test datasets, quality rubrics, failure analysis for this hiring task: support contract, contract-to-hire, and permanent searches across the us and canada?
Evidence check: Score the response on technical judgment, stated assumptions, and evidence from comparable work rather than vocabulary coverage.
Organizations hiring across San Francisco can use the market context below to shape location, compensation, and screening requirements for AI Evaluation Engineer searches.
Technical product and platform teams
Transformation and implementation programs
Internal engineering and operations teams
Systems integration and advisory teams
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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