AI/ML Engineer, Senior

Description

The company needs a senior AI/ML engineer to design, build, and deploy machine learning systems that analyze sensor data for national security missions.

This is a hands-on technical role. The company wants someone who understands how modern ML architectures work internally, can choose suitable approaches for a given problem, and can build them correctly from the ground up for a mission setting, rather than someone who has only applied off-the-shelf models to standard datasets.

The core work is using AI and ML to detect, classify, and characterize events and phenomena in sensor data, turning domain physics into automated, scalable analysis tools. You will work with scientists, algorithm engineers, and intelligence analysts to define the problem, design the solution, and deliver operational prototype code that runs reliably in classified R&D environments.

You will also act as a technical lead for junior developers, setting standards, reviewing their work, and helping them build skills.

Responsibilities

• Design and implement ML models for detecting, classifying, and characterizing events and signatures in sensor data, with enough depth to select and tune the right method instead of defaulting to the nearest framework.
• Build end-to-end ML workflows covering training data preparation, model development, evaluation, optimization, and deployment to operational or near-operational environments.
• Work closely with scientists and domain experts to understand sensor phenomenology, data characteristics, and mission needs, and turn that knowledge into sound ML design decisions.
• Write production-quality Python that is modular, documented, testable, and easy for others to maintain.
• Measure model performance rigorously using metrics that matter operationally, find failure modes and edge cases, and improve models through systematic iteration.
• Integrate ML capabilities into existing analysis pipelines and software environments, and own that integration through to deployment.
• Mentor junior AI/ML developers through code reviews, team-wide standards, identifying knowledge gaps, and developing their technical skills.
• Contribute to trade studies and architecture decisions, giving technical recommendations on ML approaches, tooling, and implementation.
• Prepare technical briefings and written documentation for customer and program stakeholders that will hold up under close review.

Requirements

• An active Top Secret clearance, with the ability to obtain SCI eligibility and pass a CI polygraph.
• A bachelor's degree in Computer Science, Electrical Engineering, Physics, Mathematics, Data Science, or a closely related technical field. An advanced degree is strongly preferred.
• At least 7 years of hands-on machine learning experience, including models you have developed and deployed in real operational or production settings, not only research prototypes.
• Deep working knowledge of modern architectures such as CNNs, transformers, and object detection frameworks, including why they work, where they fail, and how to adapt them to unusual data domains.
• Expert Python skills, including production-scale work with PyTorch, TensorFlow, or an equivalent framework.
• Experience building complete ML pipelines: data ingestion and preprocessing, annotation and labeling strategy, training, evaluation, and deployment.
• Proven ability to work with domain scientists or subject matter experts, converting technical requirements into ML designs and converting ML results into terms they can use.
• Hands-on experience mentoring or leading junior developers, including active code review, setting standards, and transferring knowledge, not only oversight.
• Strong writing and communication skills, with the ability to produce technical reports and explain findings to non-ML audiences.
• Nice to have: experience applying ML to remote sensing data such as EO/IR, hyperspectral, SAR, or similar, with a real understanding of the underlying phenomenology and not only the algorithms.
• Nice to have: background in computer vision, image classification, anomaly detection, or event characterization on scientific or sensor data.
• Nice to have: experience training on simulated or synthetic data, and knowledge of the gap between simulation and real operational data and how to handle it.
• Nice to have: familiarity with MLOps practices such as experiment tracking, model versioning, reproducible pipelines, and deployment in constrained or classified environments.
• Nice to have: experience with large geospatial or scientific datasets, and comfort with data that is noisy, sparse, or domain-specific.
• Nice to have: proficiency with scientific Python libraries such as NumPy, SciPy, pandas, OpenCV, scikit-learn, or equivalents.
• Nice to have: experience with cloud ML training environments such as AWS or Azure, and with GPU-accelerated compute.
• Nice to have: prior work supporting defense, intelligence, or classified customers.
• Nice to have: a doctorate in a relevant quantitative field such as Astronomy, Physics, Computer Science, Electrical Engineering, or a related discipline, especially where the doctoral work involved applied ML on real scientific data.

Conditions

Base pay is $200,000-$240,000 USD for positions in Virginia.

Actual pay depends on factors including location, federal contract labor categories, wage rates, experience, skills, education, and certifications.

Location and work arrangement are not stated in the posting beyond the Virginia pay range.

Benefits

• Medical, dental, and vision coverage.
• Health Savings Account with a company contribution.
• Annual paid time off and paid holidays.
• Paid parental leave.
• 401k with a generous company match.
• Coverage in areas including financial wellness, retirement planning, family assistance, and continued education.
• Training and development opportunities.
• Award programs.
• Company-sponsored events.