DoorDash is looking to build a world-class ML platform where models are developed, trained, and deployed seamlessly to improve company-wide ML workflows such as Search & Recommendations, Dasher Assignment, ETA Prediction, and Dasher Capacity Planning.
Requirements
- 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
- 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
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
- 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
- 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
Other
- 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
- Notice to Applicants for Jobs Located in NYC or Remote Jobs Associated With Office in NYC Only
