Description:

• Build and maintain data pipelines that generate training datasets for machine learning models and experimentation.
• Contribute to infrastructure that supports distributed training workflows such as PyTorch and Ray.
• Work with workflow orchestration tools such as Airflow, Flyte, or similar systems to support multi-stage ML pipelines.
• Improve reproducibility and reliability through dataset validation, monitoring, and testing.
• Partner with ML engineers to support experimentation and model iteration.
• Help optimize performance and efficiency across data processing and training systems.
• Contribute to the evolution of the offline ML platform architecture as it scales.

Requirements:

• PhD in Computer Science, Machine Learning, Systems, or a related field.
• Strong foundation in machine learning systems, distributed systems, or large-scale data processing through research or projects.
• Experience with Python and data-intensive workloads.
• Familiarity with ML frameworks such as PyTorch or TensorFlow and/or distributed systems such as Ray or Spark.
• Experience, academic or applied, with data pipelines, model training workflows, or large datasets.
• Strong problem-solving skills and ability to translate research ideas into practical systems.
• Interest in building scalable, reliable infrastructure for machine learning.
• Experience with workflow orchestration systems such as Airflow or Flyte is preferred.
• Exposure to large-scale data platforms such as data lakes, warehouses, or streaming systems is preferred.
• Publications or research in ML systems, distributed systems, or related areas are preferred.
• Strong English communication skills for professional verbal and written exchanges are required.
• Relocation support is not available for this position.
• Work visa or immigration sponsorship is not available for this position.

Benefits:

• Gross pay salary range of $112,700 to $169,100 USD.
• Comprehensive health, life, and disability insurance.
• Employee stock ownership.
• Competitive retirement or pension plans.
• Generous vacation and personal days.
• Support for new parents through leave and family-care programs.
• Mental health and wellbeing programs and support.
• Commute subsidy.
• Training and development programs.
• Volunteering and donation matching program.