Lead Machine Learning Engineer MLOps

Responsibilities

• Build, deploy, and maintain pipelines for distributed machine learning training on GPU-enabled clusters.
• Develop and manage high-throughput real-time and batch inference pipelines.
• Implement quantization techniques and deploy large language models for efficient resource utilization.
• Manage and optimize vector databases for AI and machine learning applications.
• Establish monitoring and observability pipelines for system health, model performance, and issue resolution.
• Collaborate with cross-functional teams to integrate technologies and improve infrastructure.
• Partner with product, architecture, and engineering teams on scalable and performant technical solutions.

Requirements

• Bachelor’s degree in Computer Science or a related Engineering field with 6+ years of experience, or a master’s degree with 4+ years of experience.
• Extensive experience with Python.
• Strong knowledge of data science fundamentals and training and deploying models.
• Experience with monitoring and observability tools for model inputs, outputs, and feature statistics.
• Operational experience with Ray, DuckDB, and Spark.
• Experience with containers, container orchestration, and DAG orchestration is preferred, including Docker, Kubernetes, ECS, Airflow, and Kubeflow.
• Knowledge of databases and cloud computing, preferably AWS, is preferred.
• Experience with recommendation and personalization systems is a plus.

Benefits

• Competitive total rewards package and benefits based on eligibility.
• Comprehensive health care coverage and on-site health and wellness centers.
• Retirement savings plan and backup childcare.
• Tuition reimbursement, mental health support, and financial coaching.
• Base salary, commission-based pay, and/or discretionary incentive compensation may apply depending on role eligibility.
• Work location and other compensation details are determined by role and location and provided during the hiring process.