Machine Learning Engineer

Overview

On Site
Depends on Experience
Contract - Independent
Contract - 1 Year(s)

Skills

kubernetes
Machine Learning
Neural Networks
TensorFlow
XGBoost
Python
PyTorch

Job Details

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.