Junior AI Engineer

Nou Systems•Colorado Springs, CO

About The Position

nou Systems, Inc. (nSI) is a 100% employee-owned company! We value teamwork and collaboration, and this transition allows us to take that commitment to a new level. nSI empowers our customers by supplying products, services, and expertise with powerful, data driven insights that transform defense systems and improve capabilities. We are pleased to announce the opening for an AI Engineer who will join our Technical Solutions division and become part of our rapidly growing technology company operating in missile defense, cybersecurity, test range modernization, biotechnology, and space control marketspaces. You will be part of a team designing machine learning solutions from the lab to the field.

Requirements

Bachelor’s degree in a STEM field.
Academic, internship, or professional experience with AI development (RL, XAI, LLM)
1-3 years of hands-on experience with deep learning models (supervised, unsupervised, and reinforcement learning) using PyTorch, Tensorflow, or Jax.
Must possess or can obtain an active Secret clearance.
Hands-on experience with deep learning models.
Software development fundamentals in Python.
Great written and verbal communication skills.
Team player and is glad to work on a variety of machine learning projects as opportunity presents.
U.S. citizenship is required for most positions.

Nice To Haves

Ability to write clean deep learning code in Python.
Ability and mindset to approach problems with statistical rigor.
Strong intellectual curiosity.
Comfort with self-direction.
Experience with time series data, satellite imagery, and graph neural networks.
Experience with RL development using OpenAI Gymnasium and Ray RLlib.
Experience with RL algorithms, including Q-Learning and Actor-Critic methods.
Experience with MLOps tools (PyTorch Lightning, Optuna, MLflow, etc.).
Experience with DevOps tools (Git, Gitlab, CI/CD, Remote Computing, Docker)

Responsibilities

Develop, train, and analyze machine learning models for supervised learning and reinforcement learning.
Work on a mix of projects, some of which will include other data scientists, others of which will require you to help educate non-data-science teams on the value and limitations of machine learning solutions.
Help identify and design training data sets.
Implement and customize ML models from open literature.
Develop MLOps pipelines.
Visualize and communicate your results in written documents and oral presentations.
Support multiple teams and must be able to work effectively with other teammates.