2 days ago
Base Salary
$146k - $234k/yr
Responsibilities
- Design, implement, and train causal graph models, planning algorithms, and multi-objective optimization approaches.
- Build and maintain training and inference pipelines, experiment tracking, model versioning, and dataset loaders for synthetic and real data.
- Package trained models as containerized components conforming to specified APIs and deliver code revisions with the Lead System Integrator.
- Help design synthetic-data generation and simulation campaigns covering the objective space across which models must generalize.
- Present research findings and author technical sections of status reports and design documentation.
- Coordinate with academic subcontractor researchers on shared model components.
Requirements
- At least 5 years of experience and an MS or PhD in Computer Science, Electrical Engineering, Statistics, Applied Mathematics, or a related technical field.
- At least 2 years of applied machine-learning research experience in causal inference, structural causal models, Bayesian networks, probabilistic graphical models, probabilistic programming, learned optimization, or planning.
- Strong Python software-engineering skills and production-quality PyTorch experience, including custom model implementation, training-loop design, and GPU performance profiling.
- Experience converting research prototypes into maintainable, tested, containerized code that runs unattended in an environment the developer does not control.
- Working knowledge of multi-objective and constrained optimization and experience applying it to structured design spaces.
- Experience with experiment management, reproducible ML pipelines, and dataset versioning on a multi-person research team.
- Ability to implement methods from current research literature and explain model behavior and limitations to non-ML engineers and government stakeholders.
- US citizenship is required.
- Preferred qualifications include government research-program experience, communications or digital signal-processing background, neuro-symbolic or graph-generation experience, symbolic regression, hardware-constrained inference, relevant publications, and willingness to obtain a Secret security clearance.
Categories
AI ResearchML Engineering
About Peraton
Peraton builds and integrates mission systems, cyber, space, and intelligence solutions for U.S. defense, intelligence, and civil agencies. It delivers enterprise IT, satellite and terrestrial communications, and spectrum management under government contracts. Founded in 2017, the company is headquartered in Reston, Virginia and is privately held by Veritas Capital.