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Core Competencies
Role fitCore Competencies
Use this summary to align your resume positioning with the role.
Demonstrates expertise in designing and implementing causal graph models and planning algorithms, with strong capabilities in multi-objective optimization and machine learning research. Proficient in Python and PyTorch for developing production-quality, containerized models and managing reproducible ML pipelines.
Highest-signal resume keywords
Causal InferenceMachine Learning ResearchPython Software EngineeringPyTorch Model ImplementationMulti-Objective Optimization
ATS Keywords
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Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills
Causal Graph ModelsPlanning AlgorithmsMulti-Objective OptimizationExperiment ManagementDataset VersioningProbabilistic ProgrammingContainerized CodeCustom Model ImplementationTraining Loop DesignPerformance Profiling
Soft Skills
Clear CommunicationCollaboration
Tools & Technologies
PyTorchContainerizationExperiment Tracking
Certifications & Qualifications
US CitizenshipSecret Security Clearance
Industry Keywords
Applied MathematicsStatisticsBayesian NetworksProbabilistic Graphical ModelsResearch Literature
Tech Stack
Tools & technologiesPythonPyTorch
About the role
Key responsibilities & impact- Design, implement, and train causal graph models and planning algorithms
- Architect approaches to multi-objective optimization
- Build and maintain training and inference pipelines
- Manage experiment tracking, model versioning, dataset loaders, and objectives and interfaces
- Package trained models as containerized inference components conforming to specified APIs
- Work with the Lead System Integrator on regular code drops and major code revisions
- Contribute to synthetic data generation and simulation campaigns
- 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
Requirements
What you’ll need- 5+ years of experience
- MS or PhD in Computer Science, Electrical Engineering, Statistics, Applied Mathematics, or a related technical field
- 2+ years of applied machine learning research experience
- Demonstrated work in causal inference and structural causal models, Bayesian networks and probabilistic graphical models, probabilistic programming, or learned optimization and planning
- Strong Python software engineering skills
- 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
- 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 read and implement methods from current research literature
- Ability to communicate model behavior and limitations clearly to non-ML engineers and government stakeholders
- US Citizenship
- Willingness and ability to obtain Secret security clearance
Benefits
Comp & perks- Potential eligibility for overtime
- Potential eligibility for shift differential
- Potential eligibility for a discretionary bonus
- Equal opportunity employer, including disability and protected veterans, or other characteristics protected by law