AI/ML Engineer II

Sierra Nevada Corporation•Lone Tree, CO

3d•$108,497 - $149,183•Hybrid

About The Position

The AI/ML Engineer II is a mid-level position for individuals with professional experience in designing and implementing machine learning algorithms. In this role, you will independently develop and deploy AI/ML solutions to address complex challenges, such as autonomous systems, predictive maintenance, and computer vision. You will take ownership of specific projects, perform data analysis, and optimize models for performance and scalability. This position requires a combination of technical expertise, problem-solving skills, and the ability to collaborate with multidisciplinary teams to meet mission-critical objectives. As SNC's corporate team, we provide the company and its business areas with strategic direction and business support spanning executive management, finance and accounting, operations, human resources, legal, IT, information security, facilities, marketing, and communications.

Requirements

Bachelor’s degree in computer science, mathematics, applied statistics, various engineering disciplines, or related STEM discipline
2+ years of experience in a related field.
Relevant experience can be considered as a substitute for the required educational qualifications. In the absence of a degree, a minimum of 6 years of related experience is required.
Higher level relevant degree may substitute for experience.
Practical experience using machine learning frameworks (e.g., TensorFlow, PyTorch) and applying core AI/ML techniques, including supervised, unsupervised, and introductory reinforcement learning methods.
Hands-on experience implementing and evaluating ANNs, CNNs, and RNNs in small-scale or pilot projects.
Assisted with deploying machine learning models in production or research environments.
Proficiency in programming languages such as Python, C++, C# or Java.
Strong understanding of supervised and unsupervised learning techniques.
Experience deploying AI/ML solutions in production environments.

Nice To Haves

Master’s degree in Artificial Intelligence, Machine Learning, or related field.
Experience with reinforcement learning or generative AI models (e.g., GANs, Transformers).
Working knowledge of Agile or DevOps practices in software/ML project environments.
Hands-on experience with at least one advanced ML technique (e.g., clustering or dimensionality reduction) in coursework or projects.
Basic experience with GPU programming (e.g., CUDA basics) or using GPUs for ML model training.
Exposure to generative models (e.g., GANs, Transformers) or reinforcement learning frameworks.
Experience analyzing and processing diverse datasets to extract insights.
Familiarity with requirements gathering and basic deployment of ML systems.
Awareness of hardware acceleration tools and edge AI concepts.

Responsibilities

Design, implement, and optimize machine learning models for applications such as object detection, signal processing, predictive analytics, and decision-making systems.
Develop and maintain data pipelines for collecting, preprocessing, and managing large-scale datasets.
Identify data gaps and propose solutions to improve data quality.
Conduct performance testing and validation of AI/ML models using rigorous evaluation metrics.
Optimize models for accuracy, efficiency, and scalability.
Write and deploy efficient, modular code to integrate AI/ML models into operational systems, ensuring reliability and compatibility with existing platforms.
Test AI/ML solutions in simulated environments to evaluate performance under real-world conditions.
Contribute to system-level debugging and troubleshooting.
Collaborate with hardware engineers, software developers, and systems architects to align AI/ML solutions with mission-critical requirements.
Document technical designs, workflows, and testing procedures for internal and external use.
Share findings and best practices with team members.
Explore and integrate emerging AI/ML frameworks, tools, and methodologies to enhance system capabilities and address new challenges.
Train, evaluate, and optimize standard AI models (ANNs, CNNs, RNNs) for supervised and unsupervised tasks.
Implement and test basic reinforcement learning algorithms and generative models under supervision.
Develop and integrate signal processing and computer vision modules to enhance perception and decision-making capabilities.
Conduct simulations and performance profiling of AI/ML models on CPU/GPU architectures, identifying bottlenecks.
Execute validation and verification procedures, analyze test results, and support system compliance with safety and reliability standards.