Machine Learning Engineer
Confirmed live in the last 24 hours
Tegus

501-1,000 employees

Comprehensive intelligence platform for institutional decision-making
Company Overview
Tegus stands out as a leading intelligence platform, providing unparalleled insights to institutional investors, corporations, and consultancies through its extensive database of primary and market information. The company's culture fosters creativity and passion, encouraging employees to contribute to the development of a category-defining product. Additionally, Tegus prioritizes user privacy and personalization, offering customizable website experiences and effective advertising, while maintaining a strong commitment to transparency and data security.
Data & Analytics

Company Stage

Seed

Total Funding

$111.5M

Founded

2017

Headquarters

Chicago, Illinois

Growth & Insights
Headcount

6 month growth

-3%

1 year growth

11%

2 year growth

66%
Locations
Chicago, IL, USA
Experience Level
Entry
Junior
Mid
Senior
Expert
Desired Skills
Python
Pytorch
Docker
Natural Language Processing (NLP)
CategoriesNew
AI & Machine Learning
Applied Machine Learning
Natural Language Processing (NLP)
Requirements
  • Bachelor's degree in a quantitative discipline or demonstrated experience working on data-intensive problems
  • 2 years of experience working in a data-intensive or machine learning role
  • Demonstrated experience training and deploying machine learning models with a preference towards NLP applications
  • Experience in the python data and machine learning ecosystem, including experience with pytorch
  • Basic familiarity with docker, python API frameworks like FastAPI, and software engineering best practices
  • Practical, iterative, product-focused mindset over slower, methodical, research-minded approach
Responsibilities
  • Collaborate with product managers, product engineers, and more senior ML engineers to prototype, build, and release features in the Tegus Platform that leverage the latest advances in machine learning applied to our proprietary dataset of financial text.
  • Write production python code in our internal machine learning packages and deploy production microservices.
  • Stay on top of the latest advances in machine learning, including reading and presenting research papers.