Machine Learning Engineer
On-siteSan Antonio, Texas, United States
San Antonio, Texas, United StatesOn-siteFull TimeMasters Degree
Full TimeMasters Degree
Job Summary
Design, implement, and optimize machine learning models using supervised, unsupervised, and reinforcement learning algorithms. Work with large, complex datasets to perform data preprocessing, feature engineering, and model evaluation across cloud platforms like AWS, Google Cloud, or Azure. Apply machine learning frameworks such as TensorFlow, PyTorch, and Scikit-learn to solve complex problems and improve decision-making processes. Collaborate with data scientists, engineers, and product teams to turn data into actionable insights and deploy scalable models that deliver real-world value.
Required Qualifications
- Strong understanding of machine learning algorithms (supervised, unsupervised, reinforcement learning)
- Ability to design, implement, and optimize machine learning models and workflows
- Experience working with large, complex datasets
- Knowledge of data preprocessing and feature engineering
- Familiarity with cloud-based platforms for machine learning (e.g., AWS, Google Cloud, Azure)
- Strong problem-solving skills and analytical thinking
- Proficiency in programming languages (e.g., Python, R, Java)
- Experience with machine learning frameworks (e.g., TensorFlow, PyTorch, Scikit-learn)
- Expertise in model evaluation techniques and metrics
- Strong knowledge of version control tools (e.g., Git)
- Experience with data visualization tools (e.g., Matplotlib, Seaborn, Tableau)
- Understanding of database technologies (e.g., SQL, NoSQL)
- Bachelor's Degree
- Computer Science, Data Science, Artificial Intelligence, Mathematics, Statistics, Software Engineering, Electrical Engineering, Robotics, Computational Biology, Physics
- TS/SCI Full Poly
- Full U.S. Citizenship
Desired Qualifications
- Experience with natural language processing (NLP)
- Knowledge of deep learning techniques (e.g., CNNs, RNNs)
- Familiarity with deployment tools (e.g., Docker, Kubernetes)
- Experience with data augmentation and synthetic data generation
- Ability to collaborate in cross-functional teams (e.g., engineers, product managers)
- Knowledge of edge computing and model optimization for deployment
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