Software Engineer – Machine Learning Infrastructure

Software Engineer – Machine Learning Infrastructure

Requirements

• B.S., M.S., or PhD. in Computer Science or equivalent
• Exceptionally strong knowledge of CS fundamental concepts and OOP languages
• 6+ years of industry experience in software engineering
• Prior experience building machine learning systems in production such as enabling data analytics at scale
• Prior experience in machine learning – you’ve developed and deployed your own models – even if these are simple proof of concepts
• Systems Engineering – you’ve built meaningful pieces of infrastructure in a cloud computing environment. Bonus if those were data processing systems or distributed systems

Responsibilities

• Build a world-class ML platform where models are developed, trained, and deployed seamlessly
• Work closely with Data Scientists and Product Engineers to evolve the ML platform as per their use cases
• Help build high performance and flexible pipelines that can rapidly evolve to handle new technologies, techniques and modeling approaches
• Work on infrastructure designs and solutions to store trillions of feature values and power hundreds of billions of predictions a day
• Help design and drive directions for the centralized machine learning platform that powers all of DoorDash’s business
• Improve the reliability, scalability, and observability of our training and inference infrastructure.

Preferred Qualifications

• Experience with challenges in real-time computing
• Experience with large scale distributed systems, data processing pipelines and machine learning training and serving infrastructure
• Familiar with Pandas and Python machine learning libraries and deep learning frameworks such as PyTorch and TensorFlow
• Familiar with Spark, MLLib, Databricks, MLFlow, Apache Airflow, Dagster and similar related technologies
• Familiar with large language models like GPT, LLAMA, BERT, or Transformer-based architectures
• Familiar with a cloud based environment such as AWS