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Salary: USD 124,733 - 187,048 / annual
Echodyne is seeking a Senior Machine Learning Engineer to develop machine learning capabilities for its advanced radar systems and data platforms. The company produces compact solid-state beam-steering radar technology for autonomous vehicles, uncrewed aircraft, drones, and critical infrastructure protection, combining patented metamaterial technology with software to deliver high-performance radar sensors.
In this role, you will own machine learning work end-to-end, from data exploration and model prototyping through evaluation and production deployment. You'll make practical tradeoffs among accuracy, latency, computation, and reliability to ensure real-world robustness of models deployed across Echodyne's fleet of fielded radars.
Key responsibilities include:
- Prioritizing real-world robustness and zero-regression deployment of models to production radar systems
- Improving data labeling accuracy and capacity through active learning methods and automated labeling systems
- Designing data augmentation methods that reflect realistic variation in radar deployment environments
- Enhancing track-classification models through hyperparameter tuning and architecture exploration
- Researching and developing new ML applications, such as self-supervised generative models leveraging extensive unlabeled data
- Building training and evaluation pipelines using field data, with attention to rare targets, imbalanced classes, and biased datasets
- Defining evaluation metrics and assessing model performance across target classes and field conditions
- Analyzing model failures, data gaps, and operating condition changes to improve robustness
- Partnering with cross-functional teams to integrate models into production products
Your work will directly impact the robustness and effectiveness of advanced data systems protecting critical infrastructure and people in an evolving world.
REQUIREMENTS:
- Strong foundations in machine learning, probability, statistics, and experimental design
- Experience taking machine learning solutions from prototype through production deployment
- Experience with time-series classification
- Experience training and evaluating models on imbalanced datasets
- Experience designing or assessing data augmentation for real-world sensor data
- Experience evaluating model performance, investigating failure modes, and improving data or models based on findings
- Experience deploying models on embedded, resource-constrained systems
- Strong Python software development skills and experience with TensorFlow
- Ability to independently lead open-ended technical problems and communicate results clearly
- MS degree with 4+ years or PhD with 2+ years of relevant experience in computer science, engineering, applied mathematics, or related field
- U.S. citizenship (required due to export-controlled ITAR/EAR data handling)
DESIRED EXPERIENCE:
- Experience with radar, lidar, or other spatial sensor data
- Experience with real-time processing of streaming data
- C++ experience and familiarity with production software development practices
- Experience designing labeling workflows, managing large datasets, or using active learning to improve data quality
