DoorDash USA
Staff Software Engineer, Machine Learning Infrastructure
DoorDash USA, San Francisco, California, United States, 94199
About the TeamCome help us build the world's most reliable on-demand logistics engine for delivery! We're bringing on talented engineers to help us create and maintain a 24x7, no downtime, global infrastructure system that powers DoorDash’s three-sided marketplace of consumers, merchants, and dashers.
About the RoleAt DoorDash, our Data Scientists and ML Engineers have the opportunity to dive into a wealth of delivery data to improve company-wide ML workflows such as Search & Recommendations, Dasher Assignment, ETA Prediction, and Dasher Capacity Planning. You will join a small team to build systems that empower efficient machine learning at scale. This is a hybrid opportunity in San Francisco, Sunnyvale, or Seattle.
You’re excited about this opportunity because you will…
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.
We’re excited about you because…
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.
Nice To Haves
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.
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About the RoleAt DoorDash, our Data Scientists and ML Engineers have the opportunity to dive into a wealth of delivery data to improve company-wide ML workflows such as Search & Recommendations, Dasher Assignment, ETA Prediction, and Dasher Capacity Planning. You will join a small team to build systems that empower efficient machine learning at scale. This is a hybrid opportunity in San Francisco, Sunnyvale, or Seattle.
You’re excited about this opportunity because you will…
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.
We’re excited about you because…
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.
Nice To Haves
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.
#J-18808-Ljbffr