Associate Software Engineer, RLE

Handshake was founded on a simple belief that everyone deserves a path to a great career, regardless of where they went to school or who they know. Today, we power 25 million job seekers, 1 million+ employers, and 1,600 educational institutions.

In 2025, we started Handshake AI and built the fastest-growing AI data business in history. We work directly with frontier AI lab researchers to create evaluations, publish benchmarks, and push the boundary of data. We’ve grown from $0 to ~$1B run rate and pay ~$60M to over 30K individuals every month.

Human data is the core infrastructure to AI advancement. Frontier AI labs currently improve model capabilities with various data-intensive post-training techniques. We believe that data spend for AI training will increase by 3-5x in the next few years and continue for much longer as models take on new domains. Handshake AI supports all of the frontier AI labs, working on their most complex data at the largest scale.

We’re hiring an Associate Software Engineer to help build our Reinforcement Learning Environments (RLE) platform—the systems where frontier AI models learn to complete real-world work.

This is a hands-on, growth-oriented role where you’ll contribute to building core systems, learn quickly from senior engineers, and gain exposure to cutting-edge AI infrastructure.

Location: San Francisco, CA (in-office, 5 days/week)

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  Contribute to building RLE environments and supporting infrastructure

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  Implement features across backend systems and frontend interfaces

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  Work with senior engineers to improve system reliability and performance

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  Help build modular workflow domains (e.g., engineering, finance, legal)

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  Support data pipelines that power model training and evaluation

• 0–2 years of experience (internships or full-time) in software engineering

• Familiarity with backend development and APIs

• Working knowledge of ReactJS and TypeScript

• Understanding of databases (e.g., PostgreSQL) and basic system design

• Eagerness to learn and operate in fast-paced environments

• Exposure to cloud platforms (AWS, GCP)

• Experience with data pipelines or ML-adjacent systems

• Interest in AI/ML or simulation systems

• Ships reliable features that improve RLE functionality

• Grows into owning small components independently

• Contributes to faster iteration and better developer velocity