Jobs at Platform Science

Positions 711,216

Platform Science builds a cloud platform that lets researchers publish, share, and reproduce code, data, and results. The company has become a key player in the open-science movement, partnering with universities, pharmaceutical companies, and government labs to streamline reproducible research workflows.

Hiring focuses on software engineers (frontend, backend, full-stack), data scientists, ML engineers, product managers, UX designers, and QA/DevOps specialists. Candidates can expect rigorous coding challenges, collaborative pair-programming, and the chance to influence product roadmaps that support millions of research projects.

By checking Platform Science listings on Job Transparency, you gain real salary benchmarks, employee sentiment scores, and interview tips that reduce guesswork. The platform’s data helps you negotiate confidently and match roles to your career goals.

ServiceNow CSM Senior ServiceNow Technical Consultant / Architect - Remote working USA or Canada

Company: Platform 6 Ltd

Location: United States

Posted Mar 31, 2025

You will use your knowledge to get involved in the configuration, support, process and implementation of CSM solutions along with running workshops, write…

Rapid Response Registered Nurse - Part-Time Day Shift

Company: UT Health Science Center at San Antonio

Location: San Antonio, TX

Posted Jan 29, 2025

Current Texas BON (or Compact State) RN license Upon Hire. Basic Life Support (BLS) certification Upon Hire. Mentor and educate staff of less experienced team…

AI/ML Developer

Company: Stellar Science

Location: Albuquerque, NM

Posted Jan 31, 2025

Stellar Science, an Albuquerque-based scientific software development company, is seeking talented data science and AI/ML experts. The ideal candidate will develop custom software products in various domains, including computer vision, image processing, high power microwave systems modeling, and more. The AI/ML Developer will help create novel AI/ML models, apply innovative processing and visualization techniques, and optimize performance, accuracy, and utility of modeling and simulation systems. Minimum requirements include a BS in ML/AI related field, knowledge of various machine learning applications, experience with Python and related libraries, C++ development, and data manipulation. Desired qualifications include an MS or PhD in Data Science, postdoctoral or industry experience, and experience with time series analysis, reinforcement learning, instance detection, and physics-informed neural networks.

Frequently Asked Questions

What is it like to work at Platform Science?
Platform Science fosters a culture that prizes reproducibility, transparency, and continuous learning. Employees enjoy flexible remote options, regular hackathons that focus on open-science challenges, and a flat hierarchy that encourages cross-team collaboration. Compensation includes competitive base salaries, equity, and a generous benefits package that covers health, dental, and vision. The team actively publishes research and code on the platform, giving staff a chance to see their work cited in academic papers.
What types of positions are available at Platform Science?
Platform Science hires across the tech stack: backend, frontend, and full-stack engineers; data scientists and ML engineers; product managers; UX/UI designers; quality assurance; DevOps and site reliability engineers; technical writers; and customer success specialists. Roles range from junior to senior levels, with clear career ladders and mentorship programs.
How can I stand out as an applicant for Platform Science?
Showcase reproducibility skills by sharing a GitHub project that includes data, code, and a Jupyter notebook that reproduces a published study. Highlight experience with cloud services (AWS, GCP, Azure) and containerization (Docker, Kubernetes). Emphasize collaboration by providing examples of cross-functional projects or open-source contributions. Finally, tailor your cover letter to explain how you can help Platform Science scale reproducible workflows for research partners.

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