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 while remaining deeply hands-on.
• You will build systems that can become core infrastructure for the next generation of biotech companies.

You might thrive here if

• You have taken a product, major feature, internal tool, or open-source project from zero to one.

• You can turn a vague goal into a concrete plan without waiting for someone else to break the work into tickets.

• You have strong product judgment. You know when to ship a simple version and when reliability, security, or data integrity requires deeper work.

• You are comfortable moving between a React interface, backend services, APIs, databases, queues, cloud infrastructure, and AI systems.

• You enjoy speaking with users and believe customer discovery is part of engineering.

• You take responsibility for outcomes, including bugs, failed assumptions, production issues, and adoption after launch.

• You have founder, early-stage startup, side-project, or open-source experience that shows initiative and resourcefulness.

• You learn new tools quickly. We care more about strong fundamentals and evidence of ownership than exact keyword matches.

• Experience with AWS services (ECS, Lambda, RDS, S3, CloudWatch, etc.).

• Experience with Next.js.

• Experience with Spring Boot and/or FastAPI.

• Exposure to Generative AI, LLMs, RAG systems, AI agents, or AI-powered products.

• Experience with Docker, Kubernetes, CI/CD pipelines, and infrastructure automation.

• Familiarity with compliance, governance, security, risk, or regulated industries.

• Experience working in an early-stage startup environment.

This role is probably not for you:

• You want every requirement fully specified before you begin.
• You prefer to own only the frontend, only the backend, or only the architecture.
• You measure success by tickets completed rather than customer or business outcomes.
• You optimize for perfect abstractions before learning whether users need the feature.
• You want to move into management and step away from hands-on building.
• You avoid customer conversations, production incidents, or decisions made with incomplete information.
• You need several layers of approval before shipping a sensible change.
• You want a predictable maintenance role with a fixed and narrow scope.

What we will ask you about

• Show us one product or system you personally took from idea to production.
• Tell us what was unclear at the beginning, what decisions you made, what you shipped, and what changed for users.
• Share a live product, repository, demo, architecture note, or short walkthrough when possible.
• Evidence of initiative and ownership matters more to us than employer prestige, degrees, or polished interview language.

Join Scispot if you want to build like a founder, remain close to users, and apply software and AI to problems that matter beyond software. You will have high autonomy, direct feedback, hard technical problems, and a visible link between what you ship and how quickly scientists can do their work.