TSSCI AI/ML Engineer

Actalent - Aurora, CO

Full-time - Mid Level

Aurora, CO

10,001+ employees

Administrative and Support Services

About the position

The TSSCI AI/ML Engineer position at Actalent involves developing AI/ML algorithms across various disciplines, including object detection, natural language processing, and reinforcement learning. The role emphasizes collaboration within a small team of researchers and developers, focusing on solving complex challenges for diverse customers while leveraging cloud services for model training and deployment.

Responsibilities

• Work as part of a small team consisting of developers and researchers to implement machine learning algorithms to solve a broad set of challenges for various customers.
• Analyze large multi-domain datasets such as images, text, and graph data to identify statistically relevant features to build models that provide analysts with actionable data.
• Use cloud services to train and deploy ML models.
• Review relevant publications to understand and apply cutting-edge concepts to defense and commercial applications.
• Write technical documentation supporting code, program capabilities, and user guides.

Requirements

• 2+ years of experience developing AI/ML applications, data science, and/or algorithm development.
• Proficiency in data science/ML libraries such as Scikit-learn, TensorFlow, Keras, and PyTorch (one or more).
• Experience with unsupervised and/or supervised machine learning techniques.
• Developing algorithms based on statistical analysis.
• Analyzing large datasets and building models to perform inference.
• Bachelor's degree in a STEM field with a minimum of 3 years of experience or equivalent military experience. A Master's or Ph.D. may be considered with fewer years of experience.
• Active TS/SCI clearance with CI Poly.

Nice-to-haves

• Experience with deep learning and large language models.
• Knowledge of classical machine learning techniques such as regression, Naïve Bayes, and DBSCAN.
• Understanding of Principal Components Analysis (PCA) and Singular Value Decomposition.
• Familiarity with eigenvectors and eigenvalues.