Software Engineer Ii, Reinforcement Learning Environments

Handshake Handshake · Enterprise · India · Engineering

Software Engineer II to build and scale a Reinforcement Learning Environments (RLE) platform that simulates real-world workflows for training and evaluating frontier AI models. This role involves designing and implementing backend services, data generation pipelines, and modular environment domains, with a focus on platform reliability, observability, and performance. The engineer will collaborate with US-based teams to deliver critical platform capabilities.

What you'd actually do

  1. Design, build, and scale our Reinforcement Learning Environments (RLE) platform and the infrastructure that powers it
  2. Partner closely with Engineering, Research, Product, and Operations teams in the US to execute against a shared roadmap
  3. Design and implement scalable backend services and data generation pipelines
  4. Build modular environment domains that integrate seamlessly with model training and evaluation workflows
  5. Improve platform reliability, observability, performance, and developer productivity

Skills

Required

  • 3+ years of professional software engineering experience building backend systems, distributed systems, or platform infrastructure
  • Strong proficiency with TypeScript, React, and modern backend architectures
  • Strong understanding of PostgreSQL, data modeling, distributed systems, and system design
  • Experience building and operating production systems on AWS or GCP
  • Strong problem-solving skills
  • Ability to thrive in fast-moving, ambiguous environments
  • Experience collaborating effectively with globally distributed engineering teams

Nice to have

  • Experience with reinforcement learning infrastructure, simulation systems, or AI evaluation platforms
  • Experience building internal developer platforms or workflow orchestration systems
  • Familiarity with Docker, Kubernetes, and CI/CD pipelines
  • Experience supporting applied ML or AI research teams
  • Experience working in a fast-growing startup or high-growth engineering organization

What the JD emphasized

  • Reinforcement Learning Environments
  • frontier AI models
  • model training and evaluation workflows
  • platform architecture
  • production systems

Other signals

  • building platforms for AI training
  • reinforcement learning environments
  • data generation pipelines for AI models
  • evaluating AI models