Senior​/Software Engineer Full Stack

Senior/Staff Software Engineer [Full Stack]

Prudentia Sciences is an AI-powered technology platform transforming how biopharma, biotech, and life sciences investors approach portfolio management, due diligence, and value/risk simulation. Our platform accelerates investment in breakthrough therapies by:

• Empowering biopharma to accelerate drug pipelines, maximize ROI, and achieve clinical and commercial success for greater patient impact
• Enabling strategic positioning of asset value during portfolio planning and deal making
• Equipping investors with data-driven insights to optimize capital allocation in drug asset transactions

Backed by GV (Google Ventures), Iaso Ventures, and Virtue, we’re on a mission to unlock the full potential of pharmaceutical R&D by empowering decision‑makers with real‑time, data‑driven insights.

We’re seeking an exceptional Senior/Staff Software Engineer to build and lead our core platform as we scale. This is a rare opportunity to join a high‑impact technology company in a major growth stage, where you’ll act as tech lead for our core platform, ensuring our pharma and biopharma customers achieve transformative outcomes with our platform. Our platform is quickly seeing exceptional demand in its sector, and backed by top tier life science investors who have an informed perspective on where AI can have a profound impact in the industry.

• End‑to‑End Platform Ownership:
  Design, build, and scale the web platform that enables deal teams to explore, upload, and analyze drug assets from discovery through due diligence and valuation.
• Front‑End Architecture & UX:
  Develop intuitive, data‑rich interfaces using modern frameworks (React/Next.js preferred) that empower users to manage deal pipelines, upload documents, and interpret LLM‑driven insights.
• Workflow & Orchestration:
  Implement robust backend services and job orchestration layers (e.g., FastAPI, Node, or similar) that coordinate document ingestion, model execution, and results delivery across the platform.
• Data Visualization & Insight Delivery:
  Create dynamic, interactive components that visualize scientific assessments, risk analyses and deal insights generated by ML pipelines.
• API & Integration Engineering:
  Design and maintain clean, scalable APIs between the core LLM orchestration layer and the platform. Collaborate closely with ML engineers to expose model outputs as user‑ready insights.
• Reliability & Scalability:
  Deploy and monitor platform services on AWS (or equivalent). Ensure high availability, low latency, and secure handling of sensitive scientific and deal data.
• Collaboration & Product Thinking:
  Work cross‑functionally with ML engineers, product leads, and domain experts to translate scientific and business logic into actionable workflows that drive decision‑making.
• Continuous Improvement:
  Champion engineering best practices — automated testing, CI/CD, observability, and modular architecture — while staying current on advances in AI‑driven platform development.

• Education: Bachelor’s, Master’s, or Ph.D. in Computer Science, Software Engineering, or a related technical field.
• Full‑Stack Engineering: Proven experience building modern web applications end‑to‑end — from intuitive, performant front‑ends (React, Next.js, or similar) to robust, scalable back‑ends (FastAPI, Node.js, or equivalent).
• Product & Platform Development: Hands‑on experience designing and implementing complex, data‑driven applications that integrate with APIs, asynchronous job systems, or machine learning backends.
• Frontend Architecture & UX: Strong command of component‑based design, state management, and visualization frameworks (e.g., React Query, Redux, D3, Plotly) to deliver interactive, insight‑driven user experiences.
• Backend & API Engineering: Expertise in developing RESTful or Graph
  
  QL APIs, integrating authentication/authorization, and managing event‑driven workflows and background jobs.
• Database & Data Flow: Comfort working with both relational and No
  
  SQL databases (e.g., Postgres, Mongo
  
  DB, Dynamo
  
  DB), and designing efficient data access layers for large, dynamic datasets.
• Cloud Infrastructure & Dev…