Sr AI/ML Engineer
$143,487–$197,295 year
On-siteLongmont, Colorado, United States or Herndon, Virginia, United States
Job Summary
Conduct continuous discovery and hypothesis-driven experimentation to rapidly develop prototypes assessing feasibility for mission-critical aerospace and defense applications. Design and prototype Retrieval-Augmented Generation (RAG) architectures, including embedding pipelines, retrieval strategies, and transformer-based generative components, while applying validation, safety, and explainability practices. Architect, train, and optimize advanced models including transformers, GANs, and reinforcement learning agents, supporting MPC-aligned predictive modeling and real-time decisioning systems. Provide technical leadership by mentoring engineers, guiding cross-functional teams, and ensuring compliance with regulatory and cybersecurity standards. Travel occasionally to customer sites and test facilities to support integration efforts and deliver actionable insights.
Required Qualifications
- Bachelor's degree in computer science, mathematics, applied statistics, various engineering disciplines, or related STEM discipline
- 10+ 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 12 years of related experience is required
- Higher level relevant degree may substitute for experience
- Advanced skills in machine learning frameworks (TensorFlow, PyTorch) and modern AI/ML techniques, including supervised, unsupervised, and reinforcement learning (e.g., PPO, Actor/Critic)
- Demonstrated ability to design and optimize generative AI models (e.g., transformers) and neural networks for complex applications
- Extensive experience architecting, deploying, and optimizing AI/ML systems, including ANNs, CNNs, and RNNs, in large-scale or mission-critical environments
- Led efforts to improve model performance and reliability in production settings
- Strong proficiency in programming languages such as Python, C++, C# or Java, with experience in building scalable AI/ML systems
- Demonstrated experience leading teams or projects, including mentoring junior staff
- Proven track record of deploying AI/ML models in production environments and optimizing them for real-world use cases
- Knowledge of regulatory and cybersecurity requirements for AI/ML systems in aerospace and defense applications
- Experience designing and optimizing generative AI models including transformers and GANs
- Experience building or integrating transformer-based models for retrieval-augmented or hybrid reasoning systems
- Proficiency designing embedding, retrieval, or indexing pipelines for large, multi-source datasets
- Familiarity with explainable AI (XAI) techniques for safety-critical environments
- Hands-on experience with reinforcement learning and real-time systems applicable to MPC
- Ability to obtain and maintain a Secret U.S. Security Clearance
- U.S. Citizenship status
Desired Qualifications
- Master's degree + additional years experience, or Ph.D. in Artificial Intelligence, Machine Learning, or a related field
- Experience with hardware acceleration technologies (e.g., CUDA, TensorRT) and high-performance computing systems
- Background in autonomous systems, robotics, or sensor fusion
- Familiarity with Agile/DevOps methodologies for software development
- Certifications in AI/ML or related fields, such as AWS Certified Machine Learning Specialty or Google Professional Machine Learning Engineer
- Deep understanding and practical application of Agile/DevOps in large-scale AI/ML projects
- Demonstrated experience with reinforcement learning and generative AI models in production or research settings
- Advanced proficiency in GPU programming, parallel/distributed computing, and optimizing ML workloads for performance
- Expertise in designing and implementing complex ML pipelines, including clustering, dimensionality reduction, generative modeling, and reinforcement learning, aligned to mission objectives and HMI systems
- Skilled in analyzing massive, multi-source datasets and delivering end-to-end autonomy software solutions, from requirements to deployment and maintenance
- Working knowledge of hardware acceleration technologies (CUDA, TensorRT), edge AI deployments, and explainable AI (XAI) methods
- Exposure to or interest in quantum computing for ML applications
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