Senior Scientific Platform Engineer

VivoSense, Inc.

2d•$130,000 - $160,000•Remote

About The Position

VivoSense is seeking an ambitious and highly motivated Senior Scientific Platform Engineer with a passion for learning, solving complex technical challenges, and working at the intersection of science and software engineering. As a member of a small, collaborative team, you will work closely with Science, Data Science, Product, and Engineering teams to transform scientific innovations into scalable, production-ready platform capabilities. This role requires someone who is comfortable navigating ambiguity, evaluating technical tradeoffs, and developing practical solutions that meet business and user needs. You will help operationalize and commercialize scientific workflows by integrating algorithms, digital measures, and analytical processes into the VivoSense Platform while contributing to architecture, design, implementation, validation, and continuous improvement efforts across the product ecosystem.

Requirements

Master's or Ph.D. degree in Computer Science, Software Engineering, Biomedical Engineering, Data Science, Bioinformatics, or related technical field.
5+ years of experience developing software applications, analytics platforms, scientific computing solutions, or data processing systems.
Strong proficiency in Python and experience building production-grade backend services, APIs, and data processing pipelines.
Strong analytical, problem-solving, and technical documentation skills. Comfortable with ambiguous or unknowns and able to solve problems.
Self-motivated to come up with possible solutions.
Experience developing cloud-native applications on AWS, including modern distributed, serverless, or event-driven architectures.
Ability to understand scientific methods and analytical workflows and translate them into scalable, maintainable, and production-ready software solutions.
Experience integrating analytical, scientific, or data-driven workflows into enterprise software platforms.
Experience using AI-assisted development tools to understand existing codebases, modernize legacy solutions, and improve engineering productivity.
Strong understanding of software architecture, testing, debugging, and software development best practices.
Excellent communication and collaboration skills with the ability to work effectively across Science, Data Science, Product, Quality, and Engineering teams.
Residing in one of the following states: AZ, CA, CO, FL, GA, MA, MD, NC, NJ, NV, OH, OR, PA, TN, TX.
Ability to work in the United States without sponsorship or extensions.

Nice To Haves

Experience with C#/.NET and maintaining or modernizing legacy applications.
Experience with healthcare, clinical research, wearable sensors, digital biomarkers, or physiological data.
Experience working within regulated environments such as FDA, GxP, ISO 13485, or 21 CFR Part 11.
Familiarity with R, CI/CD pipelines, DevOps practices, and Infrastructure as Code.

Responsibilities

Develop and maintain software components, data processing workflows, reporting solutions, and platform services that support study execution and customer deliverables.
Support the commercialization and operationalization of scientific innovations by transforming research outputs into robust software solutions used in clinical studies and customer-facing deliverables.
Design and implement integrations for new sensors, data sources, and platform capabilities.
Create and maintain technical designs, implementation specifications, validation documentation, and operational procedures.
Support verification, validation, testing, troubleshooting, and release activities to ensure high-quality and reliable platform functionality.
Partner with Engineering teams to design, implement, test, and deploy scientific workflows and analytical capabilities within the VivoSense’s product ecosystem.
Identify opportunities to improve platform scalability, maintainability, automation, and operational efficiency.
Collaborate with Product, Science, Data Science, Quality, and Engineering teams to define requirements, prioritize work, and drive successful project delivery.
Collaborate with Science and Data Science teams to understand digital measures, algorithms, and analytical methods developed by those teams and translate them into scalable, production-ready platform capabilities.
Integrate algorithms and digital measures developed by the Science team into the VivoSense Platform, ensuring they are reliable, maintainable, validated, and suitable for large-scale study execution.