Machine Learning Engineer

About the Role

Auron Solutions builds Physical AI and software-defined electronic warfare (EW) platforms to autonomously detect, classify, and respond to real-world signals. As Machine Learning Engineer, you will own the signal classification suite from training to field deployment — pushing models to edge AI silicon and building the MLOps pipeline that keeps them current against real-world signal data.

Key Responsibilities

• 1

  Design, train, and validate ML models for real-time RF signal classification and detection tasks.

• 2

  Deploy trained models to edge AI silicon and ensure performance in resource-constrained field environments.

• 3

  Build and maintain the end-to-end MLOps pipeline for model versioning, training automation, and continuous evaluation.

• 4

  Develop tooling to validate model performance against real-world signal data and benchmark regressions.

• 5

  Collaborate closely with the FPGA/DSP and RL engineering teams to align model outputs with hardware and decision pipelines.

Requirements

• Strong proficiency in Python and ML frameworks: PyTorch, TensorFlow, Keras, or scikit-learn.
• Experience with signal processing, RF data, or working with time-series/spectral datasets is a strong plus.
• Familiarity with edge AI deployment, model optimization (quantization, pruning), and inference pipelines.
• Ability to work independently with a high degree of ownership in a small, fast-moving startup team.
• Bonus: experience with OpenAI Gym, simulation environments, or sensor fusion pipelines.

Skills Required

How The Selection Process Works

Make Your Resume ATS-Friendly

Important Application Guidelines