Job DescriptionSAIC is seeking a skilled
Machine Learning Engineer to design, develop, and deploy machine learning (ML) models tailored to diverse applications, while delivering innovative solutions under tight deadlines. The ideal candidate will possess expertise in
Docker and containerization, with the ability to build and deploy ML models and tools in offline, standalone environments.
Key Responsibilities:
- Research and evaluate open-source ML models, fine-tuning them to meet specific operational needs.
- Design and develop custom Python-based applications (desktop and web-based) for hosting ML models.
- Experience with web-based platforms such as ComfyUI, Gradio, and Streamlit is preferred.
- Apply a solid understanding of digital signal processing (DSP) to build effect chains that transform signals to achieve desired outcomes.
- Provide rapid responses to evolving operational requirements, executing agile development to deliver customized solutions.
- Collaborate with a multidisciplinary team to address mission-critical objectives through innovative approaches.
- Leverage data science techniques to evaluate datasets, identify trends, and inform decision-making.
Required Skills & Experience:
- Proficiency in Docker for building and managing containers.
- Strong knowledge of Python programming with a focus on ML application development.
- Familiarity with DSP techniques and their practical application in signal transformation.
- Experience developing and hosting ML models using both desktop and web-based interfaces.
- Ability to work effectively in a fast-paced environment, responding to ad-hoc requests with innovative problem-solving.
Join our dynamic team to contribute cutting-edge ML solutions for high-impact projects. Your work will directly support critical mission operations in a challenging, fast-evolving environment.
QualificationsREQUIRED:
- Must be a US Citizen
- Must have an active TS/SCI with Poly clearance
- Bachelor's degree in computer science, math, data science or another technical/scientific field.
- Expertise with Python
- ML Frameworks (pytorch, tensorflow, onnx)
- Model hosting frameworks (ComfyUI, steamlit, gradio, FastAPI)
- Data science libraries (Pandas, Numpy, SciKit-learn, Scipy, matplotlib/seaborn)
- App Dev Frameworks (PyQt, tkinter)
- Experience with Docker
- Familiarity with Linux
- Familiarity with Windows OS
- Experience with version control software (Git)
DESIRED:
- Audio hardware familiarity
- Audio engineering/digital processing (DSP) familiarity
- Kubernetes familiarity
- Matlab
- C++ (Cython wrapper around C++ application)
