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Edison Scientific

AI platform for scientific R&D workflows

Engineering Manager - AI Product

Engineering LeadershipUpdated on Nov 10
Full-timeSan Francisco, CA, USA

About the Job

Requirements

  • At least 4 years of experience managing an engineering team and leading cross-functional initiatives.
  • Prior experience working as a product engineer at a high-velocity, engineering-focused organization.
  • Hands-on full-stack proficiency in Python, JavaScript or TypeScript, modern frontend frameworks, and infrastructure tooling.
  • Deep familiarity with systems and API design, product thinking, and operating applications in production.
  • Strong understanding of systems architecture, including distributed systems, data modeling, and cloud infrastructure.

Responsibilities

  • Manage and grow the product engineering team, owning mentorship, career development, and the hiring pipeline as the team scales from four to ten engineers by year-end.
  • Partner with product and research teams to bring state-of-the-art features into production.
  • Set the technical strategy for the AI product platform, focusing on engineering quality and velocity.
  • Diagnose and solve challenges scientists face when using the platform for discoveries.
  • Own technical decision-making for the API and UI layers.
  • Manage cross-functional communication and planning between engineering teams and stakeholders.
  • Lead implementation and design of the core product, influencing architecture, operations, and growth of the product engineering team.
  • Help mentor junior engineers and enable senior engineers to build software.

Desired Qualifications

  • Experience with AI applications or scientific computing solutions.
  • Prior experience in startups or small teams with significant architectural ownership and ambiguity.
  • Experience scaling systems, teams, or platforms through periods of rapid growth.

About the Company

Edison Scientific

AI platform for scientific R&D workflows

Edison Scientific provides an AI-powered platform that unifies research tasks in one environment. It combines literature synthesis, data analysis, molecular design, and the creation of publication-ready figures and reports so researchers can move from hypothesis to validated results without switching between many tools. The platform uses natural language processing and machine learning to digest scientific literature and data, automate workflows, and run tasks in parallel to speed up discovery. Compared with competitors, Edison Scientific stands out by offering an integrated, end-to-end R&D workflow that links literature, data, and design in a single platform, reducing manual handoffs and enabling scalable research. The company’s goal is to streamline R&D processes, shorten the time to insight, and help organizations rapidly validate findings and publish their work.

Company size
51-200
Company stage
Seed
Total funding
$70M
Headquarters
San Francisco, CA
Founded
2025

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