Staff Software Engineer (Full Stack)
New
P
Prudentia SciencesLife sciences technology
Fully remote in the U.S.Full-TimeStaff
Salary$230K - $275K; $230K – $275K • Offers Equity • Multiple Ranges; Tier 1 Market: San Francisco Bay Area, CA Metro, New York City (NY/NJ/PA/CT) Metro, Seattle, WA Metro $255K – $275K • Offers Equity; Boston, MA: $230K – $250K • Offers Equity
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Job Details
- Required Skills
- AWSDockerGraphQLNode.jsPostgreSQLMongoDBFastAPINext.jsReactRESTful APIs
Requirements
- Bachelor’s, Master’s, or Ph.D. in Computer Science, Software Engineering, or a related technical field.
- Experience building modern web applications end-to-end, including front ends and back ends.
- Experience designing and implementing complex, data-driven applications integrated with APIs, asynchronous job systems, or machine learning backends.
- Knowledge of component-based design, state management, and visualization frameworks.
- Expertise developing RESTful or GraphQL APIs, integrating authentication and authorization, and managing event-driven workflows and background jobs.
- Experience with relational and NoSQL databases, such as Postgres, MongoDB, or DynamoDB.
- Experience deploying full-stack applications in cloud environments such as AWS, GCP, or Azure.
- Experience with Docker, Kubernetes, Terraform, and CI/CD pipelines.
- Familiarity with secure data handling, user authentication, and compliance practices.
Responsibilities
- Design, build, and scale the web platform for exploring, uploading, and analyzing drug assets.
- Develop data-rich interfaces using React, Next.js, or similar frameworks.
- Implement backend services and job orchestration for document ingestion, model execution, and results delivery.
- Create interactive visualizations for scientific assessments, risk analyses, and deal insights.
- Design and maintain APIs connecting the platform with the LLM orchestration layer.
- Deploy and monitor platform services on AWS or equivalent cloud infrastructure.
- Ensure high availability, low latency, and secure handling of scientific and deal data.
- Collaborate with ML engineers, product leads, and domain experts to translate scientific and business logic into workflows.
- Champion automated testing, CI/CD, observability, and modular architecture.
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