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

RLE environments simulate workflows (e.g., software engineering, finance, legal) with realistic tools, constraints, and feedback loops. The data generated powers training and evaluation for model quality, robustness, and task completion.

This is a high-ownership role with direct impact on how models learn and how quickly new domains scale.

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  Build and scale our reinforcement learning environments and the platforms behind them

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  Drive architecture for scalable, reliable, extensible environment systems and data generation pipelines

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  Partner with Research, Product, and Ops to turn ambiguous needs into production systems

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  Build modular, plug-and-play domains that integrate cleanly with training and evaluation loops

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  Raise the bar on reliability, observability, performance, and data quality

• 6+ years building backend, distributed systems, or ML infrastructure

• Proficiency with ReactJS and TypeScript, with deep knowledge of backend architectures.

• Strong command of relational databases (e.g., PostgreSQL), data modeling, system design, and distributed systems principles.

• Experience with cloud infrastructure (AWS, GCP), CI/CD pipelines, and operating production systems at scale.

• Experience with RL training infrastructure, simulation systems, or evaluation platforms

• Working in an operations-heavy, tech-enabled environment

• Experience supporting applied ML or AI research teams

• RLE becomes a trusted platform for training workflow-capable models

• New domains launch quickly with high-quality data

• Systems are reliable, scalable, and drive measurable model improvements