AI Research Engineer: Causal ML & Pipelines

Peraton; Red Bank (NJ); On-site; USD 120,000 - 180,000; Full time

Job summary

Peraton Labs is seeking an AI Research Engineer to lead design and implementation of the machine learning core for extensive research and development into generative models. The role covers model formulation, training and inference pipelines, integration with synthetic and real datasets, and delivering trained, containerized frameworks.

The ideal candidate blends a research background in causal inference or probabilistic programming with strong software engineering discipline.

Qualifications

• MS or PhD in Computer Science, Electrical Engineering, Statistics, Applied Mathematics, or related field.
• 2+ years of applied ML research with causal inference or probabilistic models.
• Strong Python with production-quality PyTorch experience.
• Translate research prototypes to maintainable, containerized code running unattended.
• Knowledge of multi-objective/constrained optimization and applying to design spaces.
• Experience with experiment management and reproducible ML pipelines.
• Ability to read literature and explain model behavior to non-ML engineers and government stakeholders.
• US Citizenship.

Responsibilities

• Design, implement, and train causal graph models and planning algorithms.
• Build and maintain ML infrastructure: training/inference pipelines and experiment tracking.
• Package trained models for inference as containerized components with API compatibility.
• Contribute to synthetic data generation and simulation campaigns for objective coverage.
• Present research results at reviews and coordinate with subcontractors on shared components.

Skills

Python programming

PyTorch

Machine learning research

Education

MS or PhD in Computer Science or related field

Tools

Docker

Job description

Peraton Labs is seeking an AI Research Engineer to lead design and implementation of the machine learning core for extensive research and development into generative models. The role covers model formulation, training and inference pipelines, integration with synthetic and real datasets, and delivering trained, containerized frameworks.

The ideal candidate blends a research background in causal inference or probabilistic programming with strong software engineering discipline.