Machine Learning Engineer

Location: Irving, TX / Scottsdale, AZ / Atlanta, GA

Experience Required: 5-8 years

Position Type: Contract to Hire

About the Role: We are seeking a talented and motivated Machine Learning Engineer to join our team. The ideal candidate will have a strong background in designing, implementing, and optimizing machine learning algorithms and models, with a focus on neural networks, deep learning, NLP, and gradient boosting. This role will involve working with cutting-edge technologies and open-source machine learning frameworks such as TensorFlow, PyTorch, XGBoost, and LightGBM.

Key Responsibilities:

• Algorithm and Model Development:

  • Design, implement, and optimize machine learning algorithms and models, including neural networks (e.g., Graph Neural Networks), deep learning (e.g., Temporal Fusion Transformers), and NLP.
  • Apply gradient boosting techniques using frameworks like XGBoost and LightGBM.

• Functionality Enablement:

  • Support analysis, model optimization, statistical testing, model versioning, deployment, and monitoring.
  • Translate machine learning functionality into scalable, tested, and configurable platform architecture and software.

• Software Engineering and Platform Development:

  • Establish and adhere to strong software engineering principles for development in Python on the Azure Kubernetes Platform.
  • Ensure design decisions are aligned with scalability and industry best practices.

• Collaboration and Integration:

  • Collaborate with cross-functional teams to align machine learning initiatives with overall business goals.
  • Work closely with software engineers to integrate machine learning models into production systems.
  • Partner with data engineers to ensure the availability and quality of data for training and evaluation of machine learning models.

• Scalability and Deployment:

  • Develop strategies for deploying machine learning models at scale.
  • Ensure models are integrated into production systems with high reliability and performance.

• Performance Evaluation and Experimentation:

  • Design and conduct experiments to evaluate the performance of machine learning models.
  • Iterate on models based on feedback and evolving business requirements.

Qualifications:

• Strong experience in designing and implementing machine learning algorithms and models.
• Proficiency with open-source machine learning frameworks such as TensorFlow, PyTorch, XGBoost, and LightGBM.
• Deep understanding of neural networks, deep learning, NLP, and gradient boosting techniques.
• Proven ability to translate machine learning functionality into scalable and configurable software solutions.
• Experience with developing on the Azure Kubernetes Platform.
• Strong software engineering principles and experience with Python.
• Ability to work collaboratively in a cross-functional team environment.
• Excellent problem-solving skills and the ability to iterate on models based on feedback.
• Strong ownership of deliverables and a commitment to aligning design decisions with scalability and best practices.

Preferred Qualifications:

• Experience with Graph Neural Networks and Temporal Fusion Transformers.
• Familiarity with statistical testing, model versioning, deployment, and monitoring.
• Experience with integrating machine learning models into production systems.
• Knowledge of data engineering practices and ensuring data quality for machine learning.