What are the responsibilities and job description for the Radio Frequency Machine Learning (RFML) Product Workflow Intern, Secret Clearance Eligible position at Blue Sky Innovators?
**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 Intern. 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 and Implementation**
Blue Sky Innovators is seeking an undergraduate student or recent graduate to join our RF Machine Learning team as an RFML Product Workflow Engineer Intern. 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 and 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
- 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
- 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
- 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
- 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.
- 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
- 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
- Collaborative team environment w rking 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.)