Software Engineer I, 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 a Software Engineer to build our Reinforcement Learning Environments (RLE) platform—the interactive systems where frontier AI models learn to complete real-world work.

This role owns meaningful components end-to-end and plays a key part in scaling systems that power model training and evaluation.

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

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  Build and scale core components of RLE environments and infrastructure

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  Design and implement backend systems and data pipelines

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  Translate ambiguous product and research needs into production systems

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  Develop modular, reusable workflow domains

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  Improve reliability, observability, and system performance

• 2–4 years of experience in backend, distributed systems, or ML-adjacent infrastructure

• Strong proficiency in ReactJS and TypeScript

• Solid experience with relational databases (PostgreSQL) and data modeling

• Experience with cloud infrastructure (AWS/GCP) and CI/CD

• Ability to independently drive projects from design to production

• Experience with simulation systems or evaluation platforms

• Exposure to ML systems or working with research teams

• Experience in fast-moving, operations-heavy environments

• Owns and delivers key RLE components end-to-end

• Improves system scalability and developer velocity

• Enables faster launch of new domains with high-quality data