Staff 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 Staff Software Engineer to lead the architecture and evolution of our Reinforcement Learning Environments (RLE) platform—the systems that define how frontier AI models learn real-world work.

This is a high-impact, technical leadership role responsible for system design, cross-team alignment, and setting the long-term technical direction of RLE.

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  Define and drive architecture for scalable, extensible RLE systems and data pipelines

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  Lead development of platform capabilities enabling rapid domain creation

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  Partner with Research, Product, and Ops to shape strategy and execution

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  Set standards for reliability, observability, performance, and data quality

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  Mentor engineers and elevate engineering excellence across the team

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  Identify and solve systemic bottlenecks in scaling environments and data generation

• 8+ years of experience in distributed systems, backend infrastructure, or ML platforms

• Deep expertise in system design, data modeling, and scalable architectures

• Strong proficiency with TypeScript, backend systems, and modern web stacks

• Proven experience operating high-scale production systems in cloud environments

• Track record of technical leadership and cross-functional influence

• Experience with RL infrastructure, simulation systems, or evaluation frameworks

• Background supporting applied AI/ML research teams

• Experience building platform-level abstractions used across multiple teams

• RLE becomes a foundational, trusted platform across teams and use cases

• New domains scale rapidly with minimal engineering overhead

• Systems demonstrate step-function improvements in model quality and reliability

• Engineering velocity and system robustness increase across the organization