Software Engineer - Infrastructure
Mountain View, CA
Kumo
Kumo is a platform that uses artificial intelligence to help businesses make predictions from their data, like customer churn or product purchases.
Come and change the world of AI with the Kumo team!
Companies spend millions of dollars to store terabytes of data in data lakehouses, but only leverage a fraction of it for predictive tasks. This is because traditional machine learning is slow and time consuming, taking months to perform feature engineering, build training pipelines, and achieve acceptable performance.
At Kumo, we are building a machine learning platform for data lakehouses, enabling data scientists to train powerful Graph Neural Net models directly on their relational data, with only a few lines of declarative syntax known as Predictive Query Language. The Kumo platform enables users to build models a dozen times faster, and achieve better model accuracy than traditional approaches.
In this role, you will help us design and build the core of our system working alongside seasoned engineering leaders with decades of experience and some of the best researchers from Stanford and other top universities. We are adapting and inventing cutting edge ML techniques to fit well with learning over data warehouses. The technical challenge here is to create scalable storage and compute systems that will fit well with both the data warehouse and the ML training and inference systems and provide for scalability, reliability, restart and security of cloud-first applications. The final product will follow a customer first approach and be extremely easy to use from both ML and cloud deployment.
Companies spend millions of dollars to store terabytes of data in data lakehouses, but only leverage a fraction of it for predictive tasks. This is because traditional machine learning is slow and time consuming, taking months to perform feature engineering, build training pipelines, and achieve acceptable performance.
At Kumo, we are building a machine learning platform for data lakehouses, enabling data scientists to train powerful Graph Neural Net models directly on their relational data, with only a few lines of declarative syntax known as Predictive Query Language. The Kumo platform enables users to build models a dozen times faster, and achieve better model accuracy than traditional approaches.
In this role, you will help us design and build the core of our system working alongside seasoned engineering leaders with decades of experience and some of the best researchers from Stanford and other top universities. We are adapting and inventing cutting edge ML techniques to fit well with learning over data warehouses. The technical challenge here is to create scalable storage and compute systems that will fit well with both the data warehouse and the ML training and inference systems and provide for scalability, reliability, restart and security of cloud-first applications. The final product will follow a customer first approach and be extremely easy to use from both ML and cloud deployment.
The Value You'll Add:
- Design the core of our training and inference systems
- Design and build systems to scale and integrate
- Design APIs between the system components to decouple them and make independent development easy
- Produce designs that can be iterated over time to achieve more scalability
- Implement the first version of the product
- Engage with customers, iterate on the product
Your Foundation:
- BS (preferred MS or Ph.D.) in Computer Science or related technical discipline, or related practical experience
- Role will be focused on Systems/Scalability/Integrations.
- 2+ years experience in software design, development, and algorithm-related solutions
- 2+ years experience programming in OOP languages like Java, Python, or C++
Your Extra Special Sauce:
- Knowledge of Cloud distributed storage/databases, file systems, and distributed storage.
- Ideal candidate would have the knowledge to integrate systems and distributed data processing technologies
- Experience building Micro-services & Cloud Platforms on AWS, and Azure.
- Experience with industry, open-source projects, and/or academic research in large-data, parallel, and distributed systems.
- Experience in using and/or contributing to an inference serving technology, PyTorch, TensorFlow, etc.
- Experience in building machine learning platforms at large scale
- Understand the fundamentals of machine learning, ideally in both academic and industry environments
Benefits
- Stock
- Competitive Salaries
- Medical Insurance
- Dental Insurance
* Salary range is an estimate based on our AI, ML, Data Science Salary Index 💰
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Category:
Engineering Jobs
Tags: APIs AWS Azure Computer Science Data warehouse Distributed Systems Engineering Feature engineering Java Machine Learning OOP Open Source Pipelines Python PyTorch Research Security TensorFlow
Perks/benefits: Career development Insurance
Region:
North America
Country:
United States
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