About the role
Responsibilities
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.
Key Responsibilities:
- Design, implement, and train causal graph models, planning algorithms, as well as architect approaches to multi-objective optimization