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Role Overview

Develop and refine features for deep learning models, working with large-scale customer and behavioral datasets
Implement model architecture changes informed by recent academic research (e.g., papers from NeurIPS and similar venues)
Collaborate with client teams to understand business context and translate requirements into technical solutions
Optimize model training pipelines for efficiency and scalability
Document approaches, findings, and technical decisions for knowledge sharing across teams
Participate in code reviews and contribute to engineering best practices

Requirements

Strong proficiency with a deep learning framework (e.g. Pytorch, tensorflow)
Hands-on experience with feature engineering for predictive models
Solid foundation in machine learning fundamentals (supervised learning, neural network architectures, optimization)
Ability to read, understand, and implement techniques from ML research papers
Python proficiency in a data science/ML context
Comfortable working in ambiguous environments and adapting to unfamiliar tooling
Authorized to work where they live

Tech Stack

Python
PyTorch
Tensorflow

OpenTeams

Open Positions (1)

Engineering

Senior ML Engineer