Sr AI/ML Engineer

Sierra Nevada Corporation•Herndon, VA

1d•$143,487 - $197,295•Hybrid

About The Position

The Senior AI/ML Engineer is a highly skilled and experienced professional responsible for leading the development of complex AI/ML systems, driving innovation, and mentoring team members to deliver impactful solutions. In this role, you will oversee the design, implementation, and deployment of scalable AI/ML models for mission-critical aerospace and defense applications. You will also act as a technical leader, providing strategic guidance on AI/ML initiatives, ensuring compliance with regulatory standards, and collaborating with stakeholders to meet organizational objectives. This position demands advanced technical expertise and the ability to manage high-impact projects in a fast-paced environment. 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
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.
U.S. Citizenship status is required as this position needs an active U.S. Security Clearance for employment.
The ability to obtain and maintain a Secret U.S. Security Clearance.

Nice To Haves

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.

Responsibilities

Conduct continuous discovery and hypothesis-driven experimentation, rapidly developing prototypes to assess feasibility and potential impact.
Partner with business stakeholders to translate non-technical requirements into actionable AI/ML exploration paths.
Develop and prototype RAG-based architectures, including embedding pipelines, retrieval strategies, and transformer-based generative components.
Explore and validate new approaches for retrieval, indexing, and multimodal document understanding.
Apply validation, safety, and explainability practices in support of aerospace/defense requirements.
Design and prototype MPC-aligned models incorporating predictive modeling, optimization, and reinforcement-learning-based control.
Develop signal processing, perception, and planning pipelines supporting MPC control loops.
Use GPU acceleration, simulation environments, and HPC resources to support MPC experimentation.
Architect, train, and optimize advanced models including transformers, GANs, RL agents, and real-time systems.
Provide technical leadership, mentor engineers, and guide cross-functional teams.
Develop validation and testing frameworks ensuring compliance with safety and reliability standards.
Support integration teams with prototypes, documentation, and technical insights as required.
Contribute to AI/ML innovation and prototyping projects from exploration through technical feasibility assessment.
Support cross-functional engineering teams and integration efforts as needed.
Travel occasionally (10–20%) to customer sites, test facilities, or conferences.
Work in a hybrid office environment, balancing hands-on research with technical leadership.
Ensure compliance with safety, regulatory, and cybersecurity standards for AI/ML systems.