Frontend Engineer at Relari (W24)
$50K - $110K  •  
Testing and Simulation Stack for GenAI Systems
San Francisco, CA, US / Remote
Full-time
3+ years
About Relari

Relari is defining the new category of data-driven development infrastructure for GenAI applications. We work with top AI teams from leading startups to large enterprises, and we are growing at a blazing-fast rate. Our founding team are experts on AI system production and we are well-funded by top-tier VCs.

About the role
Skills: Figma, CSS, React, TypeScript

As a frontend engineer at Relari, you will help us solve the most critical challenges facing our society’s adoption of foundation models.

In this role, you will:

  • Spearhead the development and maintenance of high-quality user interface of our Generative AI Testing and Simulation stack.
  • Focus on user experience design, creating intuitive and innovative interfaces that meet the needs of our users.
  • Collaborate closely with backend team to integrate new features seamlessly into the user interface.
  • Work closely with the design team to translate visual concepts into functional online experiences.

You’re a good fit if

  • You are an experienced frontend engineer who’ve shipped production-grade UI/UX that have scaled to a large user base.

  • You are highly proficient in React, Next.js, TypeScript, and CSS.

  • You have a strong understanding of frontend development tools and have experience with

    responsive and adaptive design.

  • You thrive in a fast-moving environment and view unstructured environments as opportunities to identify the most impactful work and define the future success of the company.

  • You are ownership-minded. You don’t wait for company processes to get work done; instead, you proactively contribute wherever you can to drive success.

Bonus points if you have:

  • Experience building developer-facing B2B products.
  • Shipped an AI product or feature.
Technology

Our testing and simulation stack leverages a suite of foundation models and traditional machine learning techniques to generate large-scale, high-fidelity synthetic data for rigorous evaluation and systematic fine-tuning. There's a heavy research angle to our tech stack as we are on the cutting edge of defining the best practices in building reliable AI applications. We collaborate with top institutions like MIT to publish research papers and open source part of our stack (continuous-eval).

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