Large Language Model
LLM, Developer
Posted on 3/23/2023
INACTIVE
Asimov

51-200 employees

Provides tools for designing and manufacturing biological cells
Company Overview
Asimov's unique approach to biotechnology, integrating synthetic biology, computer-aided design, and machine learning, positions it as a leader in the design and manufacture of biologics and gene therapies. The company provides comprehensive support to its clients, offering both the tools for in-house cell line development and a service to create custom cell lines, demonstrating a commitment to flexibility and customer needs. Asimov's culture fosters interdisciplinary collaboration, driving technical advancements and enhancing its competitive edge in the biotech industry.
Biotechnology
B2B

Company Stage

Series B

Total Funding

$204.7M

Founded

2017

Headquarters

Boston, Massachusetts

Growth & Insights
Headcount

6 month growth

19%

1 year growth

39%

2 year growth

134%
Locations
Dorchester, Boston, MA, USA
Experience Level
Entry
Junior
Mid
Senior
Expert
Desired Skills
Python
Data Science
R
Natural Language Processing (NLP)
CategoriesNew
AI & Machine Learning
Biology & Biotech
Requirements
  • Master's degree or Ph.D. in computer science, artificial intelligence, or a related field
  • 3+ years of experience in natural language processing, machine learning, and/or data science
  • Experience working with large language models, such as GPT-3+, LLAMA, or similar
  • Experience working in synthetic biology or a related field is preferred
  • Strong communication skills and the ability to work in a team environment
  • Strong problem-solving skills and the ability to think creatively to identify new opportunities for LLMs in our products and services
  • Proficiency in programming languages such as Python or R
  • Familiarity with cloud-based infrastructure and distributed computing
Responsibilities
  • Develop and integrate LLMs into our software products, enabling natural language queries and responses to facilitate user interaction
  • Use LLMs to distill information from the scientific literature, providing summaries and key takeaways for genetic designers
  • Create educational content for genetic designers, using LLMs to generate interactive tutorials, case studies, and best practices
  • Enable greater intra-organization knowledge retrieval using LLMs, allowing team members to quickly find relevant information and insights from past experiments and other organizational documents
  • Collaborate with our team of genetic designers and software developers to identify new opportunities for LLMs in our products and services