Machine Learning Engineer II

Position Overview

We are seeking an experienced Machine Learning Engineer II to join our team and take a leading role in the development of machine learning algorithms for RF signal classification. The successful candidate will have a strong background in machine learning and signal processing, with a passion for applying cutting-edge techniques to geospatia satellite information systems.

Responsibilities

• Lead the design, implementation, and optimization of machine learning algorithms and models for RF signal classification, leveraging techniques such as deep learning, feature engineering, and anomaly detection.
• Collaborate with cross-functional teams to understand the requirements and constraints of satellite payloads, and translate them into effective ML solutions.
• Conduct research and experimentation to explore new approaches and algorithms for improving the accuracy and efficiency of signal classification in satellite communication systems.
• Work closely with hardware engineers to optimize ML models for deployment on embedded hardware payloads, considering factors such as computational resources, power consumption, and memory constraints.
• Lead performance analysis and debugging efforts to identify and address issues related to model inference, execution, and integration with embedded systems.
• Mentor junior ML engineers and contribute to the technical growth and development of the team.

Requirements

• 3+ years of experience in machine learning, with a focus on signal processing, classification, and anomaly detection.
• Proficiency in programming languages such as Python, with hands-on experience in ML frameworks (e.g., TensorFlow, PyTorch).
• Strong understanding of machine learning fundamentals, algorithms, and techniques.
• Experience with data preprocessing, feature engineering, and model evaluation in the context of satellite communication systems.
• Familiarity with embedded systems development and hardware-software co-design principles is a plus.
• Excellent problem-solving skills and ability to work in a fast-paced startup environment.
• Strong communication and leadership skills.

Preferred Qualifications

• Experience with Geospatial data and RF signal processing.
• Knowledge of digital signal processing techniques and algorithms.
• Familiarity with software development tools and version control systems (e.g., Git).
• Publications or contributions to the machine learning community.