Machine Learning Engineer

Want to build general-purpose artificial intelligence for 3D perception and model building? Common Sense Machines is developing a platform that learns to translate the visual world into a 3D simulation for people and machines. We are a seed-stage venture-backed company founded by a group of PhDs and faculty from MIT. We have worked on large AI systems at DeepMind and have collectively founded three companies with successful exits.

We are looking for a machine learning engineer to train generative models on large scale datasets and compute infrastructure. You will be part of a small but growing team of engineers and scientists building 3D generative models to capture an unprecedented level of detail and diversity of physical objects. You will have a broad range of responsibilities including:

Responsibilities

Implementing and scaling new deep learning architectures in close collaboration with researchers.; End-to-end optimization and deployment of our machine learning pipeline.; Develop software libraries and accelerators for training neural networks interfacing with search and reinforcement learning algorithms on distributed systems.; Provide, maintain and optimize high-quality Python/C++ code.

Requirements

Expert-level knowledge of PyTorch and distributed deep learning.; Proven ability to rapidly translate a research paper into prototypes (industry or research labs experience).; Experience designing, scaling, and optimizing machine learning infrastructure (e.g., distributed cloud computing, feature pipelines, visualization, MLOps, monitoring).; Team-oriented and comfortable wearing multiple hats in a growing company.