Sr Machine Learning Engineer I

Axon Axon · Enterprise · Office, WA · 2024 Dedrone R&D

This role focuses on researching, designing, and validating advanced estimation and probabilistic models for sensor fusion and tracking systems, primarily involving Kalman filters, particle filters, and Bayesian filters. The engineer will analyze real-world datasets, partner with software engineers to deploy algorithms, and stay current with relevant research.

What you'd actually do

  1. Research and develop advanced multi-sensor fusion and multi-target tracking methodologies.
  2. Design probabilistic models and state estimation frameworks for radar, RF, optical, and other sensing modalities.
  3. Develop and evaluate algorithms such as Kalman filters (EKF/UKF), particle filters, Bayesian filters, probabilistic data association, and multi-hypothesis tracking.
  4. Conduct simulation studies and performance benchmarking across varying operational conditions and noise environments.
  5. Analyze real-world datasets to validate model assumptions, quantify uncertainty, and improve robustness.

Skills

Required

  • Python
  • PyTorch, TensorFlow, or similar ML frameworks
  • MLOps practices (CI/CD for ML, model versioning, monitoring, automated retraining)
  • Statistics
  • Model evaluation
  • Performance trade-offs
  • Large, noisy, or multi-modal datasets

Nice to have

  • Bachelor’s or Master’s degree in Computer Science, Electrical Engineering, Applied Mathematics, or related field

What the JD emphasized

  • 8+ years of experience developing and deploying machine learning systems in real-world applications
  • Experience deploying ML systems in production environments (cloud, edge, or embedded systems)

Other signals

  • research, design, and validate advanced estimation and probabilistic models
  • multi-sensor fusion, multi-target tracking, and uncertainty modeling
  • translate cutting-edge theory into deployed systems