Machine Learning Engineer

Overview of Opportunity 

We are seeking a highly skilled cleared Machine Learning Engineer to design, build, and deploy advanced AI/ML systems from proof-of-concept through production. This role sits at the intersection of modern deep learning architectures, high-performance compute, and real-world deployment constraints. You will work independently or in small, highly capable teams to solve complex, loosely defined problems using cutting-edge models and infrastructure.The ideal candidate is comfortable operating with loosely defined requirements, balancing research innovation with small teams.

This is a hands-on engineering role for someone who thrives in ambiguity, enjoys deep technical challenges, and can communicate clearly across technical and non-technical audiences.

The work will be onsite in our Ellicott City, Maryland office. 

Key Responsibilities:

• Design, implement, and document proof-of-concept and production-grade machine learning systems
• Develop and optimize deep learning models across a variety of architectures, including transformer-based and convolutional approaches
• Train, fine-tune, and deploy models with a focus on performance, scalability, and efficiency
• Optimize model inference using techniques such as quantization, compilation, knowledge distillation, and distributed execution
• Work with high-performance and non-traditional compute platforms (e.g., GPUs, TPUs, CUDA-based systems)
• Collaborate closely with small teams or operate as a sole contributor on technically demanding projects
• Translate loosely defined requirements into effective technical solutions
• Clearly document designs, tradeoffs, and results for both technical and stakeholder audiences

What you will need: 

• Bachelor’s degree in relevant Computer Science/ Engineering Fields and or Mathematics or 5+ years in job experience 
• 2 years of experience writing both proof-of-concept and production level code and accompanying documentation 
• Minimum of 5 years experience with both Transformer and Convolution Neural Network architectures including:
• LLMs
• VLMs
• CNNs
• Mixture of Experts Models
• Reasoning Models
• 5 years of experience with model training and inference including:
• Model Quantization
• Model Compilation 
• Knowledge Distillation 
• Distributed Model Execution 
• 3 years of experience with PyTorch, scikit-learn, docker, git, pandas and other machine learning and machine learning adjacent technologies
• High Performance Compute or atypical compute experience (i.e. Google’s TPU, CUDA Programming)
• Experience and understanding of computer architecture, operating systems, programming languages and software vulnerabilities. 
• Experience with common reverse engineering tools, such as, IDA/Ghidra, debuggers, etc a plus
• Experience with RFML and SDRs a plus
• Experience working with embedded devices 
• 5 years experience developing software using Python, C and C++
• Ability to self-direct and work independently to include learning new technologies, languages and platforms, etc 
• Active TS/SCI clearance 

Clearance: 

• Active TS/SCI clearance

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