Frontend Engineer
Elicit
See how well this job matches your profile
Sign up to get an AI match score and generate a tailored application in seconds.
Get your match scoreTags
About the role
Role overview
As a Frontend Engineer at Elicit, you’ll help ship useful, high-quality features for researchers using a modern front-end stack. You’ll own the delivery of exciting functionality on a weekly cadence, balancing rapid iteration with maintainable systems and great user experience.
What you’ll do
- Ship front-end features for Elicit users, including:
- known feature improvements and fixes
- prototypes to validate ideas
- exploratory projects in between
- Own features in production, ensuring they are:
- scalable and resilient
- easy to operate
- Balance speed and quality by keeping smooth user workflows and a delightful UI experience.
- Collaborate on product/UX and engineering topics, contributing to discussions around UX, system design, and architecture.
- Work closely with a small team and keep user needs central to your decisions and trade-offs.
Requirements (what you bring)
- Strong CS fundamentals and ability to move comfortably around the stack.
- Proficiency in automated tests, HTML/CSS, JavaScript, React, and TypeScript.
- At least a few years of professional experience contributing to software teams and building complex web applications.
Hiring signals / technical screening topics
Be ready to discuss:
- React component re-rendering behavior
- Flexbox vs. CSS Grid and when to use each
- Web Workers: purpose and when they help
- Implementing drop-shadow effects with Tailwind
Tech stack (from the posting)
- Frontend: Next.js, TypeScript, Tailwind (also mentions Chakra)
- Backend: Node, Python
- Infrastructure: Kubernetes across a couple of clouds
- Code review/CI: GitHub
Location / travel
- Office in Oakland, CA (not all-time, but team connection is important).
- Quarterly team retreat typically in and around the SF Bay Area.
About Elicit
Elicit is an AI research assistant that helps researchers determine what’s true and make better decisions, starting with tasks like literature review. It uses language models and human-understandable task decompositions, aiming to expand safe, interpretable AI research and practical reasoning support for both experts and non-experts.
Scraped 6/17/2026