Machine Learning Engineer Intern

Who you are

• Bachelor’s degree 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 working with 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 skills and ability to translate research ideas into practical systems
• Interest in 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 areas

What the job involves

• We’re looking for a Machine Learning Engineer,Intern to join our Offline Infrastructure team
• This is an ideal role for people who is excited to work on large-scale systems and apply research-driven thinking to real-world machine learning problems
• You’ll help build and evolve the infrastructure that powers training data generation, ML workflows, and distributed model training
• Working closely with experienced engineers and researchers, you’ll contribute to systems that ensure our ML pipelines are reliable, scalable, and efficient
• This role offers the opportunity to bridge research and production—translating advanced ideas into systems that operate at scale
• Build and maintain data pipelines that generate training datasets for machine learning models and experimentation
• Contribute to infrastructure that supports distributed training workflows (e.g., PyTorch, Ray)
• Work with workflow orchestration tools (e.g., Airflow, Flyte, or similar) 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 our offline ML platform architecture as it scales

Benefits

• Comprehensive health, life and disability insurance
• Employee stock ownership
• Generous vacation and personal days
• Office food and free snacks, lots of health options!
• Employee Resource Groups
• Training and development programs
• Commute subsidy
• Competitive retirement/pension plans
• Paid leave for new parents
• Mental Health and Wellbeing programs and support
• Global Employee Assistance Program
• Volunteering and donation matching program