Early Careers: AI Automation Engineer
- Compensation
- Not disclosed
- Location
- Sunnyvale, CA, United States
- Work setup
- Onsite
- Seniority
- Intern
- Employment
- Internship
- Experience
- Not specified
- Category
- Ai engineering
- Posted
Skills
Must have
- - Must be authorized to work in the U.S. without sponsorship.
- - Bachelor's or Master's degree in Computer Science, Computer Engineering, or a related field, graduating by the start date (or equivalent practical experience)
- - Solid programming fundamentals; strong Python required. JavaScript/TypeScript is a plus
- - Experience building or consuming HTTP APIs, including LLM/AI APIs (OpenAI, Anthropic, or similar)
- - Demonstrated experience designing and evaluating prompts or LLM-based pipelines - through coursework, an internship, or a personal project. You should have built with AI models, not just used AI pro
- - Comfort with Git and a hosted platform (GitHub or GitLab): branching, pull/merge requests, code review, and CI basics
- - Basic understanding of databases and SQL
- - A testing and debugging mindset: unit tests, fixtures, end-to-end checks, and regression coverage
- - Strong attention to detail: able to read and interpret code, API documentation, logs, and specifications, and to write clear technical documentation
- - Ability to work independently on processes that are still being defined, and adapt as scope evolves
- - Ability to work onsite in Sunnyvale, CA
Nice to have
- - Experience with FastAPI, Docker, or Linux service operations
- - Exposure to scripting automation: cron jobs, workflow tools, CI-style pipelines
- - Interest or coursework in fairness and bias in machine learning
- - Experience with data pipelines, ETL, or log/telemetry processing
- - Experience with high-volume, real-time, or unstructured data sources
- - Familiarity with evaluation, scoring, or structured content systems
- - Familiarity with Fortinet products or networking/security concepts
- - Experience with observability, secrets management, or privacy/compliance practices
- Fortinet offers employees a variety of benefits, including medical, dental, vision, life and disability insurance, 401(k), 11 paid holidays, vacation time, and sick time as well as a comprehensive lea
- Wage ranges are based on various factors including the labor market, job type, and job level. Earnings for this position are expected to be $110,000 - $170,000. Exact salary offers will be determined
- All roles are eligible to participate in the Fortinet equity program, bonus eligibility is reviewed at time of hire and annually at the Company’s discretion.
Full description
About the Role
We are looking for a new graduate to join our automation engineering function. The team builds and operates internal systems that turn large volumes of operational data into reliable, auditable workflows - and applies LLM-based evaluation only where output quality can be measured.
You will work across the team's current systems and future automation projects, applying the same design playbook - validation, idempotency, evaluation, human review - to every workflow you touch. This is not just integration work: you will design the pipelines, define what "correct" and "well-scored" mean, measure output quality against ground truth, and catch the ways these systems get things wrong.
This role comes with high ownership: we define the contract and test plan before implementing, keep deterministic logic where possible, and document what we build. You do not need to know every item in our stack on day one; you do need to learn quickly and ask good questions.
This is a role we expect to grow - the shape of that growth is still being defined.
Key Responsibilities Systems & Pipelines
- Own the team's services end to end: deployment, monitoring, troubleshooting, and runbooks
- Build and harden ingestion pipelines: API integrations, retries, idempotency, validation, and handling of late or incomplete data
AI Workflows & Evaluation
- Design prompts and pipelines for summarization, classification, retrieval, and scoring
- Calibrate against human-graded samples; measure accuracy, consistency, and bias
- Ship AI features only when they meet the quality bar, with human review for high-stakes or low-confidence decisions
Content & Data Quality
- Maintain structured content, metadata, and validation checks
- Review content for leakage, staleness, and drift, and track pipeline health over time
Workflow Automation
- Automate end-to-end processes from intake through reporting
- Turn manual, repetitive workflows into reliable tools and help move repositories to the internal Git platform
Data Stewardship & Security
- Handle sensitive data responsibly: redaction, access control, retention, auditability, and secrets management
- Document prompts, rules, and decision logic; improve observability, CI, and operational tooling
REQUIRED QUALIFICATIONS
- Must be authorized to work in the U.S. without sponsorship.
- Bachelor's or Master's degree in Computer Science, Computer Engineering, or a related field, graduating by the start date (or equivalent practical experience)