Radio Frequency Machine Learning (RFML) Product Workflow Engineer, Secret Clearance Eligible

Position Overview

Blue Sky Innovators is seeking an undergraduate student or recent graduate to join our RF Machine Learning team as an RFML Product Workflow Engineer. This role offers a unique opportunity to architect and implement the complete data pipeline from raw RF signal ingestion through machine learning-based identification and fingerprinting. You'll work at the intersection of signal processing, machine learning, and software engineering to build production-grade systems that advance the state-of-the-art in RF domain analysis.

Responsibilities

Pipeline Architecture & Implementation

• Design and implement end-to-end data workflows from raw IQ data ingestion through RFML model inference
• Build robust data processing pipelines that handle various RF signal formats (SIGMF, raw IQ files, etc.)
• Architect scalable fingerprinting systems that extract and store unique RF signal characteristics
• Develop fingerprint matching and retrieval systems to identify previously observed signals
• Implement monitoring and logging throughout the pipeline to ensure data quality and system reliability

ML Integration & Optimization

• Integrate transformer-based and other neural network models into production workflows
• Optimize data preprocessing and feature extraction for ML model inputs
• Implement efficient batching and inference strategies for real-time or near-real-time processing
• Collaborate with ML researchers to transition models from development to production

Data Management & Storage

• Design database schemas for storing signal fingerprints and associated metadata
• Implement efficient similarity search and matching algorithms for fingerprint comparison
• Build APIs and interfaces for querying the fingerprint database
• Manage data versioning and provenance throughout the workflow

Software Engineering Best Practices

• Write clean, maintainable, and well-documented Python code
• Develop comprehensive unit and integration tests for pipeline components
• Use configuration management tools (e.g., Hydra) for flexible system parameterization
• Implement CI/CD practices for continuous deployment and testing
• Create visualization and reporting tools for pipeline performance metrics

Required Qualifications

• Currently pursuing or recently completed a BS/BA in Computer Science, Software Engineering, or related field
• Strong programming skills in Python with experience in software design patterns
• Understanding of data structures and algorithms
• Experience with version control (Git) and collaborative development workflows
• Ability to read technical documentation and learn new technologies independently
• Strong problem-solving skills and attention to detail
• Due to the nature of the work, you must be eligible for a security clearance and must be a U.S. Citizen.

Preferred Qualifications

• Coursework or projects in machine learning, signal processing, or data engineering
• Experience with scientific Python libraries (NumPy, SciPy, pandas)
• Familiarity with ML frameworks (PyTorch, TensorFlow)
• Knowledge of database systems (SQL, vector databases, or time-series databases)
• Experience with containerization (Docker) and orchestration tools
• Understanding of RF fundamentals or digital signal processing concepts
• Experience with package managers and virtual environments (conda, uv, pip)
• Familiarity with configuration management and experiment tracking tools

What You'll Learn

• Advanced RF signal processing and machine learning techniques
• Production ML system design and deployment
• Large-scale data pipeline architecture
• Domain expertise in RF fingerprinting and emitter identification
• Collaborative research and development in a cutting-edge technical domain

Work Environment

• Collaborative team environment working alongside RFML engineers and researchers
• Hands-on experience with state-of-the-art RF datasets and ML models
• Opportunities to contribute to novel research and development
• Flexible development environment with modern tooling (GitLab, VSCode, Jupyter, etc.)