Founding Engineer @ Vela | Solving Scheduling | India

Who are we? We are a team of obsessive thinkers, builders, and doers from Harvard, CMU, UChicago, UPenn, GT, and more. We have published papers, own patents, built companies, and received international accolades. Most of all, we are a group of friends (some would say family) who work hard (9-9-6) to solve some of the most challenging problems faced by real people. What do we do? Vela’s mission is to completely remove the tedious human coordination starting with scheduling. We started with the most complex version of the problem faced by searchers, recruiters, and staffers by creating systems that are able to solve situations like ‘John the recruiter will gladly take a call with CEO Jill at 7:00 AM in the morning, but not with their colleague’. How does one intelligently make decisions as such based on the context that changes person to person, industry to industry? ← This is just one of the many problems we tackle everyday. Who are you? Ultra intellectually curious (you are active in competitions/championships such as ICPC, IOI, INOI/ICOz, etc; or do research for funzies; or win hackathons etc.) Have crazy amounts of agency (passion projects 4 life; things annoy you? - you just fix them) Love working with friends (extremely strong opinions, weakly held; have a great sense of humor) Build agents and are an AI power user (but can think for yourself) Fast learner Who we would love for you to become (or you already are): Curious about Machine Learning and Reinforcement Learning Agent evaluator Test driven developer What do you get to do? Solve problems that don't have a known solution yet. Build agents that make thousands of context-dependent decisions daily. Ship things that real paying customers use the next day. Talk to customers, understand their needs, and build. Own entire systems end-to-end: from the research that informs an approach, to the architecture, to the code, to watching it work in production. Work at the frontier of what agents can actually do reliably, not what demos look like on Twitter. How do you understand personal preferences/priorities in calendars? How do you make agents that communicate on your behalf across channels (voice, chat, email: not just with you but on behalf of you with people)? What context do you share and not share? How do you share enough context for efficacy? All powered by ML models, LangGraph, In house memory layers, etc. Behavioral interview Work trial