Lead ML Data Engineer, AI Core

Key Responsibilities

As a Lead Machine Learning Engineer in AI Core Data Intelligence, you will:

• Design and build scalable data ingestion pipelines that bring new datasets into our AI Core platform, ensuring reliable, efficient data flow from source to model training. - Implement data quality monitoring and validation systems that catch issues before they impact model performance, maintaining the health of datasets across our ML ecosystem. - Model new types of data into our foundation models. - Analyze the impact of new data sources on existing models, conducting experiments to measure performance improvements and guide data acquisition decisions. - Develop and maintain data preparation workflows that transform raw data into features ready for model training, working with distributed computing frameworks like Ray. - Tune and optimize machine learning models when new datasets are integrated, applying hyperparameter optimization and evaluating model performance improvements. - Collaborate with AI Core ML, Platform, and Infra teams to ensure seamless data flow across our ML infrastructure, from ingestion to model deployment. - Lead technical initiatives that improve our data engineering practices, setting standards for data quality, pipeline reliability, and model-data integration. - Mentor team members and contribute to hiring activities, helping build a strong and diverse team that drives innovation in AI infrastructure. #### Basic Qualifications

• Typically 6+ years of experience in machine learning engineering, data engineering, or related fields with a strong track record of building production data and ML systems. - Proven experience designing and building data ingestion pipelines at scale, with expertise in distributed computing frameworks (Ray, Spark, or similar). - Strong background in applied machine learning, including model training, hyperparameter tuning, and performance evaluation. - Experience analyzing how data changes impact model performance, with the ability to design and run experiments to measure improvements. - Proficiency in Python for data engineering and ML workflows, with experience working with large-scale data processing systems. - Solid understanding of data quality principles and experience implementing monitoring, validation, and alerting systems. - Strong problem-solving skills with the ability to address complex, ambiguous problems requiring coordination across multiple teams. - Excellent communication skills, capable of explaining technical concepts to both technical and non-technical stakeholders. - Demonstrated leadership experience, including mentoring team members and contributing to technical decision-making. #### Preferred Qualifications