Machine Learning Scientist

Summary

Join GoDaddy's Data and Artificial Intelligence team as a Machine Learning Scientist! Help develop cutting-edge ML models for Pricing and Generative AI projects. Collaborate with ML engineers to build robust pipelines and ensure the reliability and scalability of our solutions. This is an evergreen posting with opportunities at various levels, from mid-level to senior. Your level will be determined through the interview process. We welcome candidates from diverse backgrounds. Contribute to real-world impact by working alongside platform teams to deliver impactful solutions. We are always seeking talented Machine Learning Scientists to join our team.

Requirements

• Mid-Level: 4+ years of experience, including 2+ years in machine learning or related fields
• Senior-Level: 7+ years of industry experience in software engineering and 4+ years in machine learning or related fields
• Proficiency in Python and machine learning algorithms, with experience in supervised/unsupervised learning, deep learning, NLP, and computer vision
• Experience deploying and monitoring ML models in production using AWS services such as SageMaker
• Hands-on experience building APIs for ML model serving with frameworks like Flask, FastAPI, or Django
• Strong understanding of software engineering best practices (version control, CI/CD, code reviews)
• Ability to work both independently and collaboratively in cross-functional teams
• Experience writing unit tests and documentation for ML code

Responsibilities

• Design and implement advanced machine learning algorithms for Pricing and Generative AI projects using PyTorch and TensorFlow
• Develop and deploy machine learning models using Python and other state-of-the-art ML frameworks
• Build APIs to deliver real-time and batch-mode predictions to customers, leveraging AWS, Docker, and cloud technologies
• Monitor and optimize the performance, accuracy, and reliability of our ML solutions with logging, metrics, and dashboards
• Research and evaluate new ML techniques to enhance existing solutions and discover new opportunities
• Fine-tune models to improve performance, scalability, and adaptability
• Analyze complex datasets to inform model development and ensure accuracy
• Stay at the forefront of advancements in machine learning and AI, incorporating new methodologies as needed
• Collaborate with cross-functional teams to integrate ML solutions into broader product and platform initiatives
• Contribute to the development of standardized processes for model evaluation, validation, and production deployment
• Lead the exploration and adoption of innovative ML technologies, maintaining our competitive edge

Preferred Qualifications

• Experience working with large-scale, complex datasets
• Applied machine learning experience in industries like e-commerce, finance, or healthcare
• Familiarity with ML lifecycle management tools such as MLflow, Kubeflow, or Airflow
• Expertise with common ML libraries like TensorFlow, PyTorch, Keras, and Scikit-learn
• Hands-on experience with large language models (LLMs) and prompt engineering
• Proficiency in multiple programming languages
• Familiarity with containerization technologies like Docker and orchestration tools like Airflow

Benefits

• Paid time off
• Retirement savings (e.g., 401k, pension schemes)
• Bonus/incentive eligibility
• Equity grants
• Participation in our employee stock purchase plan
• Competitive health benefits
• Other family-friendly benefits including parental leave