AI/ML Research Engineer

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

Required Qualifications:

• 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, NumPyro, 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 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 the current research literature, and to communicate model behavior and limitations clearly to non-ML engineers and government stakeholders
• US Citizenship

Desired Qualifications:

• Prior work on IARPA, DARPA, or similar government research programs with independent T&E, leaderboard-based evaluation, and a regular delivery cadence
• Background in communications, digital signal processing, or information theory, including familiarity with modulation, coding, channel models
• Experience with neuro-symbolic methods, program synthesis, or graph generation models (graph neural networks, autoregressive graph generators, or diffusion over graphs)
• Familiarity with symbolic regression (PySR or similar) or with generating executable code or domain-specific language artifacts from learned models
• Experience with hardware-constrained inference (model compression, quantization, or deployment on embedded or edge compute)
• Publications in causal inference, probabilistic machine learning, or machine learning for wireless communications
• Willingness and ability to obtain Secret security clearance

Peraton Labs isseeking 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.

Key Responsibilities:

• 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