AI​/ML Research Engineer

Peraton is a next-generation national security company that drives missions of consequence spanning the globe and extending to the farthest reaches of the galaxy. As the world's leading mission capability integrator and transformative enterprise IT provider, we deliver trusted, highly differentiated solutions and technologies to protect our nation and allies. Peraton operates at the critical nexus between traditional and nontraditional threats across all domains: land, sea, space, air, and cyberspace.

The company serves as a valued partner to essential government agencies and supports every branch of the U.S. armed forces. Each day, our employees solve the most daunting challenges that our customers face. Visit  to learn how we're keeping people around the world safe and secure.

Peraton is a next-generation national security company that drives missions of consequence spanning the globe and extending to the farthest reaches of the galaxy. As the world's leading mission capability integrator and transformative enterprise IT provider, we deliver trusted, highly differentiated solutions and technologies to protect our nation and allies. Peraton operates at the critical nexus between traditional and nontraditional threats across all domains: land, sea, space, air, and cyberspace.

The company serves as a valued partner to essential government agencies and supports every branch of the U.S. armed forces. Each day, our employees solve the most daunting challenges that our customers face. Visit  to learn how we're keeping people around the world safe and secure.

Peraton Labs is seeking an AI Research Engineer to lead the design and implementation of the machine learning core for extensive research and development into generative models. The role focuses on the research-to-code path for model generation, planning, and optimization, including formulating models, building the training and inference pipelines, integrating with synthetic and laboratory datasets, and delivering trained, containerized frameworks.

The ideal candidate combines a research background in causal inference, probabilistic programming, or Bayesian methods with strong software engineering discipline. They must be comfortable working in a domain where model outputs must be explainable and verifiable. Familiarity with communications and digital signal processing is preferred but not required at the outset; the ability to learn the domain quickly and collaborate closely with RF, DSP, and formal methods engineers is required.

• Design, implement, and train causal graph models, planning algorithms, as well as architect approaches to multi-objective optimization
• Build and maintain the ML infrastructure: training and inference pipelines, experiment tracking, model versioning, dataset loaders for synthetic and real data, and the objective and interface
• Package trained models for inference as containerized components that conform to the specified API, working with the Lead System Integrator to deliver regular code drops and major code revisions
• Contribute to the design of synthetic data generation and simulation campaigns so that training data covers the objective space the models must generalize across
• Present research results at program design reviews, site visits, and PI workshops; author technical sections of monthly status reports and design documentation; coordinate with academic subcontractor researchers on shared model components

• 5+ years of experience and an MS or PhD in Computer Science, Electrical Engineering, Statistics, Applied Mathematics, or a related technical field
• 2+ years of applied machine learning research experience with demonstrated work in at least one of: causal inference and structural causal models, Bayesian networks and probabilistic graphical models, probabilistic programming (Pyro, Num Pyro, Stan, PyMC, or similar), or learned optimization and planning
• Strong Python software engineering skills with production-quality use of PyTorch, including custom model implementation, training loop design, and performance profiling on GPUs
• Experience translating research prototypes into maintainable, tested, containerized code that runs unattended in an environment the developer does not control
• Some working knowledge of multi-objective and constrained optimization (evolutionary, Bayesian, gradient-based, or combinatorial methods) and experience applying it to…