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)
- 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 products
- 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
PREFERRED QUALIFICATIONS
- 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 leave program.
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 by factors such as the candidate's subject knowledge, skill level, qualifications, experience, and geographic location.
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.
About UsFortinet makes possible a digital world that we can always trust through its mission to protect people, devices, and data everywhere. This is why the world’s largest enterprises, service providers, and government organizations choose Fortinet to securely accelerate their digital journey. The Fortinet Security Fabric platform delivers broad, integrated, and automated protections across the entire digital attack surface, securing critical devices, data, applications, and connections from the data center to the cloud to the home office. Ranking #1 in the most security appliances shipped worldwide, more than 615,000 customers trust Fortinet to protect their businesses. And the Fortinet NSE Training Institute, an initiative of Fortinet’s Training Advancement Agenda (TAA), provides one of the largest and broadest training programs in the industry to make cyber training and new career opportunities available to everyone.
Skills Required
- Bachelor's degree in Computer Science or a related field, or equivalent practical experience
- Solid programming fundamentals in Python, JavaScript, TypeScript, or a similar language
- Experience working with APIs, including LLM and AI APIs
- Experience designing and evaluating prompts or pipelines for LLM-based tools
- Proficiency with Git and a hosted platform such as GitHub or GitLab, including branching, pull requests or merge requests, code review, and CI basics
- Basic understanding of databases, such as SQL
- Strong attention to detail and technical writing ability
- Ability to work independently on evolving processes and adapt as scope changes
- Familiarity with test or assessment platforms or ATS integrations
- Exposure to scripting automation, workflow tools, cron jobs, or CI-style pipelines
- Interest or coursework in fairness or bias in machine learning systems
- Prior experience conducting or coordinating technical interviews
Fortinet Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about Fortinet and has not been reviewed or approved by Fortinet.
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Equity Value & Accessibility — Equity in the form of stock awards and RSUs is highlighted as a meaningful part of total compensation and one of the package’s strongest advantages. Recognition programs may include stock, further emphasizing equity’s role in rewards.
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Strong & Reliable Incentives — Performance bonuses and incentives are presented as key components of total rewards, with some roles achieving strong total compensation through these elements. Incentive opportunities are described as a major strength when included in an offer.
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Retirement Support — A 401(k) plan, often with matching contributions, and broader retirement and savings programs contribute to solid financial security. These elements are positioned as competitive features of the package.
Fortinet Insights
What We Do
Fortinet develops and sells cybersecurity solutions.
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