Lead Software Engineer
Posted on 3/29/2024
INACTIVE
Insitro

201-500 employees

Machine learning-driven drug discovery and development
Company Overview
Insitro stands out as a pioneering drug discovery and development company, utilizing machine learning and high-throughput biology to transform the traditional, often inefficient, process of creating medicines. The company's unique approach combines human genetics, functional genomics, and machine learning to construct predictive in vitro human cell-derived disease models, aiming to deliver more effective medicines faster and at a lower cost. With a diverse team of experts in life sciences, machine learning, human genetics, engineering, and drug discovery, insitro fosters a collaborative culture that integrates the languages of biology and machine learning, making it an ideal workplace for those seeking to advance tomorrow's medicines.
AI & Machine Learning
Data & Analytics

Company Stage

Series C

Total Funding

$743M

Founded

2018

Headquarters

South San Francisco, California

Growth & Insights
Headcount

6 month growth

4%

1 year growth

23%

2 year growth

58%
Locations
San Bruno, CA, USA
Experience Level
Entry
Junior
Mid
Senior
Expert
Desired Skills
Data Analysis
CategoriesNew
Backend Engineering
Full-Stack Engineering
Software QA & Testing
Software Engineering
Requirements
  • Master's degree in Computer Science or related field
  • 2 years of experience in a similar position
  • Bachelor's degree in Computer Science or related field
  • 5 years of progressive experience in a similar position
Responsibilities
  • Work closely with a team of scientists and engineers to design and develop lab software infrastructure
  • Identify challenges and solutions to improve lab data applications and integrations
  • Design and implement rigorous and scalable landscape of applications and data processing pipelines
  • Design, implement, and maintain scalable backends and intuitive frontends for capturing, extracting, integrating, and analyzing large volumes of scientific and lab operational data
  • Contribute to building state-of-the-art machine learning infrastructure
  • Evaluate new technologies, practices, and vendors to increase scientific capabilities and efficiencies
  • Ensure solutions fit appropriately into the information ecosystem and ensure the integrity of data architecture