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Prudentia Sciences

AI-driven life sciences due diligence platform

Staff Software Engineer - Full Stack

Full-Stack EngineeringConfirmed live in the last 24 hours
Full-time

About the Job

Requirements

  • Bachelor’s, Master’s, or Ph.D. in Computer Science, Software Engineering, or a related technical field.
  • Proven experience building modern web applications end-to-end, including performant front ends using React, Next.js, or similar technologies and scalable back ends using FastAPI, Node.js, or equivalent.
  • Hands-on experience designing and implementing complex, data-driven applications that integrate with APIs, asynchronous job systems, or machine learning back ends.
  • Strong command of component-based design, state management, and visualization frameworks such as React Query, Redux, D3, or Plotly.
  • Expertise in developing RESTful or GraphQL APIs, integrating authentication and authorization, and managing event-driven workflows and background jobs.
  • Comfort working with relational and NoSQL databases such as Postgres, MongoDB, and DynamoDB, and designing efficient data access layers for large, dynamic datasets.
  • Experience deploying full-stack applications in cloud environments such as AWS, GCP, or Azure using DevOps practices including Docker, Kubernetes, Terraform, and CI/CD pipelines.
  • Familiarity with secure data handling, user authentication, and compliance best practices; healthcare, life sciences, or enterprise experience is especially valuable.
  • Strong communication and collaboration skills, and ability to work closely with ML engineers, product managers, and scientific domain experts.
  • Strong problem-solving skills and an analytical mindset.
  • Passion for continuous learning, rapid prototyping, and iterating based on user needs.
  • Autonomous, self-starter attitude with a strong sense of ownership.
  • Ability to explain technical ideas clearly to non-technical audiences.

Responsibilities

  • 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.
  • Develop intuitive, data-rich front-end interfaces using modern frameworks such as React or Next.js to help users manage deal pipelines, upload documents, and interpret LLM-driven insights.
  • Implement backend services and job orchestration layers, such as FastAPI or Node, to coordinate document ingestion, model execution, and results delivery across the platform.
  • Create dynamic, interactive components that visualize scientific assessments, risk analyses, and deal insights generated by machine learning pipelines.
  • Design and maintain scalable APIs between the core LLM orchestration layer and the platform, and collaborate with ML engineers to expose model outputs as user-ready insights.
  • Deploy and monitor platform services on AWS or an equivalent cloud platform, ensuring high availability, low latency, and secure handling of sensitive scientific and deal data.
  • Work cross-functionally with ML engineers, product leads, and domain experts to translate scientific and business logic into actionable workflows.
  • Champion automated testing, CI/CD, observability, and modular architecture, and stay current on advances in AI-driven platform development.

Desired Qualifications

  • React and Next.js are preferred front-end frameworks.
  • Experience in healthcare, life sciences, or enterprise environments is especially valuable.

About the Company

Prudentia Sciences

AI-driven life sciences due diligence platform

Prudentia Sciences provides an AI-native platform for due diligence and dealmaking in the life sciences sector, serving biopharma, biotech, and financial buyers such as venture capital and private equity firms. The platform merges artificial intelligence (including large language models and deep learning) with scientific expertise and financial modeling to speed asset evaluation, offering rapid data-room synthesis, real-time valuation simulations, and scenario planning with a human-in-the-loop for expert input. It differentiates itself by tightly integrating AI analytics with domain knowledge and financial modeling to identify risks and optimize trade-offs across time, cost, probability of success, and revenue, enabling faster go/no-go decisions. Its goal is to de-risk investments, shorten deal cycles, and scale the platform globally, expanding into Asia-Pacific and Europe with backing from seed and Series A funding led by GV, Iaso Ventures, Virtue, and McKesson Ventures.

Company size
11-50
Company stage
Series A
Total funding
$27M
Headquarters
Cambridge, MA
Founded
2023

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