Machine Learning Engineer I

Position Overview:

We are seeking a talented Machine Learning Engineer I to join our team and contribute to the development of machine learning algorithms for RF signal classification. The successful candidate will play a key role in designing, implementing, and optimizing ML models to run on embedded hardware payloads deployed on satellites.

Responsibilities:

• Collaborate with senior ML engineers and domain experts to understand the requirements and challenges of RF signal classification for satellite applications.
• Design and implement machine learning algorithms and models for signal processing, feature extraction, and classification.
• Conduct data preprocessing, feature engineering, and model evaluation to ensure the accuracy and robustness of the ML models.
• Optimize ML models for efficient execution on resource-constrained embedded platforms, considering factors such as power consumption and memory constraints.
• Work closely with hardware engineers to ensure seamless integration of ML models with embedded systems and hardware accelerators.
• Conduct performance analysis and debugging to identify and address issues related to model inference and execution on embedded platforms.
• Stay updated with the latest advancements in machine learning techniques and satellite communication technologies.

Requirements:

• Solid understanding of machine learning fundamentals and algorithms.
• Proficiency in programming languages such as Python, with experience in ML libraries (e.g., TensorFlow, PyTorch).
• Experience with data preprocessing, feature engineering, and model evaluation techniques.
• Familiarity with embedded systems development and hardware-software co-design principles is a plus.
• Strong problem-solving skills and ability to work in a startup environment.
• Excellent communication skills and ability to collaborate effectively with cross-functional teams.

Preferred Qualifications:

• Optional experience with RF signal processing.
• Knowledge of digital signal processing techniques and algorithms.
• Familiarity with software development tools and version control systems (e.g., Git).
• Passion for space technology and a desire to contribute to the future of satellite communication.