Senior Machine Learning Engineer

Responsibilities

• Drive end-to-end lifecycle management of AI/ML projects from concept and data acquisition to prototyping, model development, deployment, optimization and ongoing maintenance.
• Implement and champion best practices in MLOps, including data collection, model training pipelines, model deployments, monitoring, alerting, and QA to ensure model reliability and performance.
• Contribute significantly to model architecture decisions, leveraging state-of-the-art machine learning, deep learning, and reinforcement learning techniques.
• Develop and deploy robust feature engineering pipelines and ML services optimized for low latency and high throughput.
• Establish and utilize robust A/B testing and experimentation frameworks to evaluate and iteratively improve model performance.
• Translate research papers into high-quality, production-ready code.
• Communicate effectively, collaborate, and build long-term relationships across the organization.

Qualifications

• PhD with 3+ years of experience or MS with 3-5+ years of industry experience in AI/ML, developing and deploying production-grade ML systems.
• 2+ years of experience in building AI/ML models in at least one of the following domains: Ads, relevance, ranking, recommendation systems, and search.
• 3+ years of experience in building distributed, low-latency, high-throughput batch and online ML services.
• 3+ years of experience in deploying and maintaining ML pipelines in production, including feature engineering and model monitoring frameworks.
• 2+ years of experience in Python and proficiency with distributed frameworks (Spark, Hadoop), SQL, and cloud infrastructure.
• 2+ years of experience with ML packages such as Tensorflow or PyTorch, scikit-learn, and Spark ML.
• Able to operate efficiently in a high-paced, multi-functional, and rapidly evolving environment.