AI/ML Research Engineer

Peraton•Red Bank, NJ

23h

About The Position

We are 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.

Requirements

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

Nice To Haves

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

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