ML Infrastructure Engineer – Early Career/Internship

This is a fully remote position, open to applicants in Washington.

📋 Description

• Develop and sustain data pipelines that create training datasets for machine learning models and experimentation.

• Contribute to the infrastructure that facilitates distributed training workflows (e.g., PyTorch, Ray).

• Utilize workflow orchestration tools (e.g., Airflow, Flyte, or similar) to enable multi-stage ML pipelines.

• Enhance reproducibility and reliability through dataset validation, monitoring, and testing.

• Collaborate with ML engineers to aid in experimentation and model iteration.

• Assist in optimizing performance and efficiency across data processing and training systems.

• Play a role in the development of our offline ML platform architecture as it scales.

⛳️ Requirements

• Bachelor’s degree in Computer Science, Machine Learning, Systems, or a related discipline.

• Solid foundation in machine learning systems, distributed systems, or large-scale data processing (acquired through research or projects).

• Proficiency in Python and experience handling data-intensive workloads.

• Familiarity with ML frameworks (e.g., PyTorch, TensorFlow) and/or distributed systems (e.g., Ray, Spark).

• Experience (academic or applied) with data pipelines, model training workflows, or large datasets.

• Strong problem-solving abilities and the capacity to translate research concepts into practical systems.

• Enthusiasm for building scalable, reliable infrastructure for machine learning.

• Nice to Have

• Experience with workflow orchestration systems (Airflow, Flyte, etc.).

• Exposure to large-scale data platforms (data lakes, warehouses, streaming systems).

• Publications or research in ML systems, distributed systems, or related fields.

🏝️ Benefits

• Comprehensive health, life, and disability insurance.

• Commute subsidy.

• Employee stock ownership.

• Competitive retirement/pension plans.

• Generous vacation and personal days.

• Support for new parents through leave and family-care programs.

• Office food snacks.

• Mental Health and Wellbeing programs and support.

• Employee Resource Groups.

• Global Employee Assistance Program.

• Training and development programs.

• Volunteering and donation matching program.