Machine Learning Engineer/Applied Scientist
Search & Recommendations
Posted on 3/21/2024
Biorender

201-500 employees

Online platform for creating scientific illustrations
Company Overview
BioRender stands out as a leading company in the scientific communication sector, offering a unique platform that simplifies the creation of scientific graphics. Their competitive edge lies in their extensive library of pre-made icons and templates spanning over 30 fields of life sciences, making it accessible and user-friendly for scientists across disciplines. The company's commitment to enhancing science communication, backed by a team of dedicated engineers, illustrators, and entrepreneurs, fosters a dynamic work culture that values hard work, attention to detail, and a shared passion for science.
Energy

Company Stage

Seed

Total Funding

$17.2M

Founded

2017

Headquarters

Toronto, Canada

Growth & Insights
Headcount

6 month growth

9%

1 year growth

34%

2 year growth

150%
Locations
Remote in USA • Remote
Experience Level
Entry
Junior
Mid
Senior
Expert
Desired Skills
Agile
Python
Tensorflow
Pytorch
Java
Scala
CategoriesNew
AI & Machine Learning
Applied Machine Learning
AI Research
Requirements
  • Extensive industry experience as an ML engineer
  • Hands-on experience with traditional keyword-based search technologies and modern search paradigm
  • Experience with deep learning frameworks such as PyTorch and TensorFlow
  • Experience with data exploration, analysis, and feature engineering
  • Excellent programming skills with Python, Scala, or Java
  • Expertise with operationalizing, monitoring, and scaling machine learning models in cloud ecosystems
  • Previous experience working cross-functionally with product and engineers in an agile environment
  • Experience building a variety of ML applications end to end
  • Familiar with the state-of-the-art deep learning and AI research
  • Familiar with the state-of-the-art ML/AI research with publication track record
Responsibilities
  • Design and execute multi-quarter ML initiatives
  • Oversee the performance and optimization of search engine and recommendation systems
  • Prototype, optimize, and productionize ML models
  • Evaluate performance of search and recommendation systems and models
  • Influence the company’s ML system and data infrastructure
  • Collaborate closely with product managers, scientists, full-stack engineers, and designers
  • Communicate with business, data, and engineering counterparts
  • Propose recommendations to maximize business impact