Sr AI/ML Engineer

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.

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.
• 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.
• Familiarity with explainable AI (XAI) techniques for safety-critical environments.
• Hands-on experience with reinforcement learning and real-time systems applicable to MPC.

We Prefer

• 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.
• Background in 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.
• 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.
• Exposure to or interest in quantum computing for ML applications.