29 days ago
Bengaluru, IndiaMid Level
Responsibilities
- Design, build, and scale the Reinforcement Learning Environments platform and its supporting infrastructure.
- Develop scalable backend services, data-generation pipelines, and modular environment domains integrated with model training and evaluation workflows.
- Improve platform reliability, observability, performance, and developer productivity.
- Partner with Engineering, Research, Product, and Operations teams across India and the US to deliver shared roadmap objectives.
- Participate in technical design and architecture decisions and write high-quality, maintainable code.
Requirements
- 6+ 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 operating production systems on AWS or GCP.
- Ability to solve problems and collaborate effectively with globally distributed engineering teams.
- Preferred experience with reinforcement learning infrastructure, simulation systems, AI evaluation platforms, internal developer platforms, workflow orchestration, Docker, Kubernetes, CI/CD pipelines, or applied ML and AI research teams.
Benefits
- Generous equity grant vesting over four years.
- Housing bonus of 1.3 Lakhs spread throughout the first year.
- Performance bonus ranging from 10–100% of base.
- Medical insurance coverage.
- Food credit for every in-person day.
- In-office role in Bengaluru, India.
Categories
About Handshake
Handshake builds a career network and recruiting platform that connects college students and recent grads with employers, sold as SaaS to universities and subscriptions/solutions to employers. Founded in 2014 and headquartered in San Francisco, it serves 1,600+ educational institutions and over 1 million employers. The company also operates Handshake AI, which partners with frontier AI labs on human data collection and evaluations for model training and post-training workflows.