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.

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

- Automate the end-to-end workflow: question selection -> test delivery -> answer collection -> scoring -> reporting

- Help transition our source code and repositories to an internal Git platform

- Track test results to help establish scoring benchmarks over time

Quality & Fairness

- Track scoring consistency and accuracy over time; report on pipeline health

- Watch for and mitigate AI evaluation bias across candidate demographics or answer styles

- Document the process, prompts, and scoring logic for auditability

Growth & Scope

- Apply the same automation and AI-integration skills developed here to other internal workflows as the role expands

- Partner with engineering teams to identify manual, repetitive processes that are good candidates for automation

- Take on additional internal tooling and infrastructure projects as capacity and scope grow

REQUIRED QUALIFICATIONS

- Bachelor's degree in Computer Science or related field, or equivalent practical experience

- Solid programming fundamentals (Python, JavaScript/TypeScript, or similar)

- Comfort working with APIs, including LLM/AI APIs (OpenAI, Anthropic, etc.)

- Demonstrated experience designing and evaluating prompts/pipelines for LLM-based tools (not just using AI products, but building with them)

- Proficient with Git and a hosted platform (GitHub or GitLab) - branching, PRs/MRs, code review, CI basics

- Basic understanding of databases (SQL or similar)

- Strong attention to detail and technical writing ability

- Ability to work independently on processes that are still being defined, and adapt as scope evolves

PREFERRED QUALIFICATIONS

- Familiarity with test/assessment platforms or ATS integrations

- Exposure to scripting automation (e.g., workflow tools, cron jobs, CI-style pipelines)

- Interest or coursework in fairness/bias in ML systems

- Prior experience conducting or coordinating technical interviews (not required, but a plus)

Must be authorized to work in the U.S. without sponsorship.

The US base salary range for this full-time position is $101,600-$124,200. 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 labour market, job type, and job level. 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 the time of hire and annually at the Company’s discretion.