Job Description
Why Scispot Scispot began after my brother Guru and I (Satya) watched someone they loved run out of time while slow, manual lab processes delayed a promising treatment. We are building Scispot so life-saving science can move at software speed. Biotech & Lifescience teams should not have to choose between moving fast and keeping their data clean, connected, traceable, and ready for AI. We are building the digital backbone for scientific discovery. Scispot connects lab operations, instrument data, scientific workflows, and AI-driven insights in one platform. This becomes the memory layer for lifescience teams for their agents. Your code will not optimize clicks for another consumer app. It will help scientists run experiments faster, trace samples accurately, automate repetitive work, and move treatments closer to patients. This is a rare chance to build infrastructure at the intersection of software, AI, data, and biology. What You'll Do Own major product areas from first customer conversation to production. You will define the problem, choose the architecture, build the interface and backend, deploy it, measure its impact, and keep improving it. Build the GLUE and API layers that reliably move data from lab instruments, external systems, and scientific files into Scispot. Let scientists query the full journey of a sample—from batch to run to assay—using natural language, graph-backed data, and grounded retrieval. Build AI agents that can take useful actions inside lab workflows, not merely produce text. Improve recommendation, search, memory, and retrieval systems across OpenSearch, vector databases, graph databases, and LLM pipelines. Turn one-off lab instrument integrations into reusable connectors, SDKs, templates, and eventually a self-serve connector ecosystem. Build polished workflows across React and TypeScript, Java Spring Boot and Python services, APIs, queues, data stores, and AWS infrastructure. Improve EKS infrastructure, automated tests, telemetry, incident response, and system reliability as Scispot grows. Speak with scientists and lab operators regularly. Learn how their work actually happens. Then ship tools that remove real bottlenecks. Turn incomplete requirements and messy scientific workflows into simple, reliable software without waiting for a perfect PRD.
