Deep Learning Engineer

NBCUniversal•New York, NY

13h•$160,000 - $175,000

About The Position

We are seeking a Deep Learning Engineer with experience manipulating large 2D and 3D media datasets. In this role, you will implement core algorithms that sit at the intersection of computer vision and computer graphics, helping us turn high dimensional data into high-fidelity content.

Requirements

Graduate degree in Data Science, Computer Science, or a related field (or equivalent deep technical experience).
Proven experience as a DL Engineer or Applied Research Engineer in a fast-paced environment.
Fluency with Python, Git, and the Unix shell.
Proven experience training and debugging artificial neural networks or adjacent experience (e.g., gradient descent, nonlinear optimization, or classical machine learning).
A strong mathematical background covering linear algebra, statistics, probability, and numerical methods.
Effective collaboration and the ability to work closely with a founding team.
High attention to detail and the ability to meet key R&D milestones in an early-stage startup environment.

Nice To Haves

Prior experience in industries with complex multi-disciplinary teams such as robotics, smart grids, precision agriculture, game development, or aerospace is highly valued.
Preferred prior experience with modern C++ to interface with data ingestion and product pipelines.

Responsibilities

Implement core deep-learning, computer vision, and (inverse-)procedural modeling algorithms in Python, relying on mathematical techniques from linear algebra, probability, and geometry.
Apply cutting-edge research in machine learning and computer graphics to solve real-world problems.
Work closely with our cofounders to understand high-level product vision and translate customer requirements into technical milestones.
Interact with remote machines via a Unix shell to deploy and test code on large-scale geospatial datasets, ultimately generating 3D content for our customers.
Use Git to manage source code and modularize complex implementation tasks into manageable, executable components.