Job description

ABOUT PRUDENTIA SCIENCES 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 dealmaking - Equipping investors with data-driven insights to optimize capital allocation in drug asset transactions Backed by GV (Google Ventures), McKesson, SignalFire, 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. THE OPPORTUNITY We're seeking an exceptional 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. WHAT YOU'LL DO - 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. WHO YOU ARE Core Qualifications: - 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 GraphQL APIs, integrating authentication/authorization, and managing event-driven workflows and background jobs. - Database & Data Flow: Comfort working with both relational and NoSQL databases (e.g., Postgres, MongoDB, DynamoDB), and designing efficient data access layers for large, dynamic datasets. - Cloud Infrastructure & DevOps: Experience deploying full-stack applications in cloud environments (AWS, GCP, or Azure) using modern DevOps practices — including Docker, Kubernetes, Terraform, and CI/CD pipelines. - Security & Compliance Awareness: Familiarity with best practices for secure data handling, user authentication, and compliance (especially valuable in healthcare, life sciences, or enterprise environments). - Collaboration & Product Mindset: Strong communication and collaboration skills; ability to work closely with ML engineers, product managers, and scientific domain experts to deliver elegant, high-impact user workflows. Soft Skills: - 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. - Excellent communication skills—able to explain technical ideas clearly to non-technical audiences. - Collaborative team player with a desire to build things that truly matter. Bonus - Experience in healthcare, life sciences, or biopharma sectors is nice, but not required. More important is a willingness to dive into the field and a curiosity to learn about life sciences and the drug development process, and how deal making revolves around it. WHAT WE OFFER - Impact: Your work directly enables breakthrough therapies to reach patients faster - Ownership: Build and lead the platform as a foundational engineering leader - Growth: Shape our product experience during a critical scaling phase - Compensation: Competitive salary and equity - Team: Collaborative, mission-driven colleagues passionate about transforming biopharma LOCATION We are open to exceptional talent that is fully remote in the U.S. but have strong preference towards someone who can come in our Boston, San Francisco or New York City office twice per week (Tues/Thurs).

Requirements

Must-haves

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.
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 GraphQL APIs, integrating authentication/authorization, and managing event-driven workflows and background jobs.
Database & Data Flow: Comfort working with both relational and NoSQL databases (e.g., Postgres, MongoDB, DynamoDB), and designing efficient data access layers for large, dynamic datasets.
Cloud Infrastructure & DevOps: Experience deploying full-stack applications in cloud environments (AWS, GCP, or Azure) using modern DevOps practices — including Docker, Kubernetes, Terraform, and CI/CD pipelines.
Security & Compliance Awareness: Familiarity with best practices for secure data handling, user authentication, and compliance (especially valuable in healthcare, life sciences, or enterprise environments).
Collaboration & Product Mindset: Strong communication and collaboration skills; ability to work closely with ML engineers, product managers, and scientific domain experts to deliver elegant, high-impact user workflows.
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.
Excellent communication skills—able to explain technical ideas clearly to non-technical audiences.
Collaborative team player with a desire to build things that truly matter.
We are open to exceptional talent that is fully remote in the U.S. but have strong preference towards someone who can come in our Boston, San Francisco or New York City office twice per week (Tues/Thurs).

Nice-to-haves

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.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

aireactnext.jsllmmlapiawsci/cdnode.jsmachine learninggraphqlpostgresgcpazuredockerkubernetes

Platform: ashby
Effort: quick