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.

First 30 days

Join customer calls and map one complete lab workflow, including the instruments, data handoffs, user decisions, and current sources of manual work.
Ship a production improvement to a live customer workflow. It could improve instrument ingestion, scientific search, sample traceability, workflow automation, or Scibot.
Audit one critical path across the interface, APIs, data layer, and infrastructure. Find the highest-risk reliability or scaling issue and fix at least one part of it.

First 60 days

Own and launch one meaningful full-stack feature or instrument integration. Handle the scope, architecture, implementation, tests, telemetry, rollout, and customer feedback.
Improve one core AI or data path, such as recommendations, retrieval, ETL, search, or sample chain of custody.
Define a clear success metric for your work. Examples include time saved, fewer manual steps, lower error rates, faster processing, stronger adoption, or better system reliability.

First 90 days

Become the clear owner of a core product area such as Scibot, GLUE and APIs, instrument integrations, scientific search, or chain of custody.
Turn lessons from several customer implementations into a reusable platform capability rather than another one-off solution.
Present a technical plan for the next two quarters. Explain the customer need, architecture, risks, trade-offs, milestones, and expected impact.
Raise the engineering bar through better tooling, tests, observability, documentation, or deployment workflows that help the full team move faster.

Why this role is different

You will work directly with the founders and influence product direction, technical architecture, hiring, and how Scispot builds.
You will be trusted with real production ownership early. There is no long waiting period before your work matters.
You will hear directly from scientists when something you built saves them time—or fails to solve their problem.
You will work across product engineering, AI, data systems, infrastructure, and customer discovery instead of being confined to one narrow layer.
You will have room to challenge the roadmap. Strong reasoning and customer evidence matter more than title or seniority.
You will help shape the engineering culture of a small team

Full Stack AI Engineer at Scispot