qualifications: 8+ years of industry experience in infrastructure, platform, data, or ML engineering, with a deep background in designing production infrastructure for data-intensive or AI/ML systems. Hands-on expertise building and operating MLOps systems (for model development, training, and deployment) and managing GPU infrastructure, including scheduling, resource management, and utilization. Proficient in managing data infrastructure technologies (e.g., BigQuery, data warehouses, object storage, orchestration systems like Dagster or Airflow) and operating within Kubernetes/containerized environments. Demonstrated ability as a player-coach, including people-management experience or leading small engineering teams, with a focus on mentoring senior engineers and influencing technical direction. Strong communication skills with the ability to partner effectively across diverse groups, including Data Engineering, AI/Research, Product Engineering, Security, Bioinformatics and Leadership.
