×
Register Here to Apply for Jobs or Post Jobs. X

AI Automation Engineer

Job in Sunnyvale, Santa Clara County, California, 94087, USA
Listing for: Zoomcar
Full Time position
Listed on 2026-08-18
Job specializations:
  • Software Development
    AI Engineer (Applied/Software)
Salary/Wage Range or Industry Benchmark: 110000 - 160000 USD Yearly USD 110000.00 160000.00 YEAR
Job Description & How to Apply Below

We're looking for a junior engineer to be the primary engineer building and maintaining our technical interview pipeline - from the question bank to AI-assisted scoring to full workflow automation - working alongside the team to grow this role into a broader automation function over time. This isn't a "call an API and ship it" job: you'll be designing the systems that add/calibrate interview questions and evaluate candidate answers, which means understanding how to prompt and structure AI models for consistency, how to measure and validate output quality against ground truth, and how to catch and correct the ways these systems get things wrong.

This is a role we expect to grow - the shape of that growth is still being defined.

Fortinet (NASDAQ: FTNT) secures the largest enterprise, service provider, and government organizations around the world. Fortinet empowers its customers with intelligent, seamless protection across the expanding attack surface and the power to take on ever-increasing performance requirements of the borderless network - today and into the future. Only the Fortinet Security Fabric architecture can deliver security without compromise to address the most critical security challenges, whether in networked, application, cloud or mobile environments.

Fortinet ranks number one in the most security appliances shipped worldwide and more than 500,000 customers trust Fortinet to protect their businesses.

We are committed to providing reasonable accommodations for all qualified individuals with disabilities. If you require assistance or accommodation due to a disability, please contact us at
Fortinet is an equal opportunity employer. We value diversity in our company, and all qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, age, military/veteran status or any other applicable legally protected characteristics in the location in which the candidate is applying.

We're looking for a junior engineer to be the primary engineer building and maintaining our technical interview pipeline - from the question bank to AI-assisted scoring to full workflow automation - working alongside the team to grow this role into a broader automation function over time. This isn't a "call an API and ship it" job: you'll be designing the systems that add/calibrate interview questions and evaluate candidate answers, which means understanding how to prompt and structure AI models for consistency, how to measure and validate output quality against ground truth, and how to catch and correct the ways these systems get things wrong.

This is a role we expect to grow - the shape of that growth is still being defined.

KEY RESPONSIBILITIES Assessment Repository Management
  • Add, revise, and retire technical interview questions across roles/levels/skill areas
  • Maintain question metadata (difficulty, topic tags, expected answer criteria, role relevance)
  • Create variant/randomized versions of questions to reduce answer-sharing risk
  • Build validation checks (automated and manual) to catch incorrect, ambiguous, or miscalibrated questions before they enter the repository
  • Build automation to update problem sets and recalculate average difficulty ratings as new results come in
  • Review and update questions periodically to prevent leakage/staleness and keep content aligned with actual job requirements
Scoring & Answer Evaluation
  • Architect scoring workflows that evaluate and compare candidate answers against reference answers/rubrics, grounded in a clear methodology for what "correct" and "well-scored" mean
  • Calibrate and benchmark scoring prompts/models against human-graded samples, measuring accuracy and identifying systematic errors or bias
  • Understand and account for model failure modes (inconsistency, hallucination, prompt sensitivity) and design safeguards around them
  • Flag edge cases or low-confidence scores for human review rather than fully automating high-stakes decisions
Process Automation #J-18808-Ljbffr
To View & Apply for jobs on this site that accept applications from your location or country, tap the button below to make a Search.
(If this job is in fact in your jurisdiction, then you may be using a Proxy or VPN to access this site, and to progress further, you should change your connectivity to another mobile device or PC).
 
 
 
Search for further Jobs Here:
(Try combinations for better Results! Or enter less keywords for broader Results)
Location
Increase/decrease your Search Radius (miles)
0
200
Filters
Education Level
Experience Level (years)
Posted in last:
Salary