ENSCO, Inc.
Machine Learning Engineer
About this role
ENSCO seeks an experienced Machine Learning Engineer to develop advanced ML/DL solutions for national security applications. You'll work with senior scientists on complex problem sets involving signal processing, data fusion, and algorithm design while mentoring junior team members.
What you'll do
- Design and implement machine learning and deep learning models to solve complex technical problems
- Build datasets and conduct controlled experiments to assess algorithm performance
- Extract features from structured and unstructured data for ML/DL applications
- Collaborate with senior scientists to understand problem requirements and data parameters
- Mentor junior ML engineers and provide technical guidance
- Develop and deploy ML solutions using modern frameworks and DevOps practices
What they're looking for
- Machine learning and deep learning frameworks (PyTorch, TensorFlow/Keras, scikit-learn)
- Python or Matlab programming
- Signal processing and data fusion
- Linux environment navigation and programming
- Large dataset management and analysis
- Feature extraction and algorithm development
- Docker/Kubernetes (desired)
- CI/CD pipeline experience (desired)
Benefits
- Hybrid work arrangement
- Full-time regular employment
- Salary range: $73,923–$131,331 USD annually
- U.S. security clearance sponsorship
- Work on national security missions
- Opportunity to mentor and lead junior engineers
Opens the application — the Jobs AI extension fills it for you. Set up autofill
Opens the official application on the employer’s site. No login required.
ENSCO, Inc.
ENSCO develops specialized sensor electronics, mechanical systems, test equipment, and avionics control systems for government and private clients. The company is hiring mechanical engineers, software developers, test equipment engineers, software verification engineers, and SharePoint administrators to support hardware design, cloud-based applications, avionics testing, and enterprise collaboration infrastructure.
- Website
- ensco.com
Likely interview questions
- Walk us through a specific machine learning or deep learning project where you extracted relevant insights from a large, complex dataset. What frameworks did you use and what challenges did you face?
- Describe your experience with signal processing and feature extraction. How have you applied these techniques to improve model performance in past projects?