Senior Machine Learning Engineer, webAI

• Active US Security clearance or eligibility and willingness to obtain a US Security clearance
• 5+ years of experience in applied AI, ML engineering, or production AI systems
• Deep proficiency in PyTorch, TensorFlow, or Hugging Face Transformers
• Proven experience deploying AI models across cloud, edge, and mobile hardware environments
• Expertise in model compression and optimization (quantization, pruning, distillation)
• Strong understanding of GPU computing, CUDA, and performance profiling
• Experience building RAG pipelines and integrating vector databases (e.g., FAISS, Milvus, Pinecone)
• Familiarity with multi modal models and synthetic data generation methods
• Low level programming experience in C, C++, or Rust with understanding of computer memory and concurrency
• Strong algorithmic and problem solving skills, especially in distributed or constrained compute environments

Desirable

• We are seeking an Senior Machine Learning Engineer to support our Public Sector initiatives focused on building and optimizing production ready AI systems for secure and distributed environments
• This role sits at the intersection of machine learning, systems engineering, and deployment optimization, bridging research and real world implementation
• You will be responsible for transforming prototype models into scalable, efficient, and reliable production systems that operate seamlessly across a spectrum of hardware from government cloud infrastructure to edge devices in restricted or disconnected environments
• Productionize AI models from research prototypes into scalable, deployable systems used in real world applications
• Develop, fine tune, and optimize models using PyTorch, TensorFlow, or Hugging Face Transformers, adapting both open and closed source models
• Implement model optimization techniques such as quantization, pruning, distillation, and hardware specific acceleration
• Engineer systems for dynamic model adaptation using low rank adaptation (LoRA), parameter efficient fine tuning (PEFT), and on device inference strategies
• Build and maintain Retrieval Augmented Generation (RAG) pipelines, including vector database integration for contextual retrieval
• Work with multi modal AI systems across computer vision, audio, and natural language domains
• Employ synthetic data generation and digital twinning techniques (GANs, diffusion models, or simulation based) to create robust datasets for edge cases
• Develop GPU accelerated and low level system code in C, C++, or Rust for performance critical operations
• Optimize model execution for distributed and resource constrained environments, ensuring reliability under variable connectivity conditions
• Collaborate cross functionally with Infrastructure, MLOps, and Security teams to deliver secure, compliant, and high performance AI solutions for government partners