Staff Software Engineer, Rle

Handshake · Enterprise · Remote · Engineering

Staff Software Engineer to lead the architecture and evolution of Handshake's Reinforcement Learning Environments (RLE) platform, focusing on scalable systems, data pipelines, and enabling rapid domain creation for frontier AI models. This role involves technical leadership, system design, and cross-team collaboration to ensure reliability, observability, and performance.

What you'd actually do

  1. Define and drive architecture for scalable, extensible RLE systems and data pipelines
  2. Lead development of platform capabilities enabling rapid domain creation
  3. Partner with Research, Product, and Ops to shape strategy and execution
  4. Set standards for reliability, observability, performance, and data quality
  5. Mentor engineers and elevate engineering excellence across the team

Skills

Required

  • 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

Nice to have

  • 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

What the JD emphasized

  • lead the architecture and evolution of our Reinforcement Learning Environments (RLE) platform
  • technical leadership
  • system design
  • cross-team alignment
  • setting the long-term technical direction
  • scalable, extensible RLE systems and data pipelines
  • rapid domain creation
  • reliability, observability, performance, and data quality
  • systemic bottlenecks in scaling environments and data generation

Other signals

  • leading architecture for reinforcement learning environments
  • building scalable data pipelines for AI training
  • supporting frontier AI labs