Machine Learning Engineer, Offline Infrastructure (Entry-Level / New Grad PhD)

Work from home Full-time role Hiring

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:

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