Handshake - Manager Software Engineer
Requirements
• Engineering leader + builder: experience scaling engineering organizations while remaining technically engaged • Strong people leadership: experience hiring and developing senior engineers; experience managing engineering managers is a strong plus • Experience building engineering teams and organizations through periods of rapid growth • Experience partnering closely with US-based engineering teams or globally distributed engineering organizations • Execution in ambiguity: proven ability to align cross-functionally and deliver in fast-moving, unclear problem spaces • Systems + product mindset: strong platform/distributed systems background, and the ability to turn research and operational needs into a clear roadmap, ship iteratively, and measure outcomes • Experience with RL training infrastructure, simulation systems, or evaluation platforms • Human-in-the-loop systems (annotation, rubric tooling, QA pipelines, workflow platforms) • Operations-heavy, tech-enabled environment experience • Familiarity with AWS/GCP, APIs, Docker, and modern stacks (TypeScript/Node, React) • Experience building systems used by applied ML or AI research teams • What Success Looks Like • Build a world-class engineering organization in India that becomes a core extension of Handshake AI Engineering • RLE becomes the default platform researchers use to train workflow-capable models • New domains launch quickly and reliably with trusted quality gates • Environment reliability and data quality are trusted inputs into training and evaluation decisions • The India engineering team scales with strong technical leaders who can independently drive new verticals • The platform measurably improves real-world task completion, robustness, and quality
Responsibilities
• Build, hire, and develop a high-performing engineering team building RL environments and the platform behind them • Partner closely with Engineering, Research, Product, and Operations teams in the US to execute against a shared roadmap • Drive architecture for scalable, reliable, extensible environment systems and data generation pipelines • Build modular, plug-and-play domains that integrate cleanly with training and evaluation loops • Raise the bar on reliability, observability, performance, and data quality • Create a culture of ownership, speed, and strong engineering fundamentals in an ambiguity-heavy setting
Benefits
• Shape how every career evolves in the AI economy, at global scale, with impact your friends, family and peers can see and feel • Partner hand-in-hand with world-class AI labs, Fortune 500 partners and the world’s top educational institutions • Work together with engineers, scientists, operators, and more from Palantir, Meta, Scale AI, and former YC founders • Build a massive, fast-growing business with billions in revenue • 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 are building our India team to help accelerate the development of frontier models. This team is a critical, strategic investment for us - we have grown the team 3x in the past six months to help fuel our next phase of growth. India-based teammates will work hand-in-hand with US-based teams to scope, execute, and deliver critical human data projects to Frontier Labs and other customers. • 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. • Reinforcement Learning Environments (RLE) • 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. • high-ownership role • Generous Equity Grant vested over 4 years • Housing Bonus: 1.3 Lakhs spread throughout the first year • Well Defined Performance Bonus ranging between 10 - 100% of base • Medical Insurance Coverage • Food credit for every in person day.
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