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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.
We’re looking for motivated and talented new grads to join us in the following areas:
Data Engineering
Infrastructure Engineering
Distributed Systems Engineering
You'll have the opportunity to work alongside world-class engineers, researchers, and leaders from top institutions like Stanford.
The Value You'll Add:
- Design & Build: Help design the core systems for model training, inference, and scaling, working on everything from system architecture to APIs.
- Innovate: Contribute to the development of cutting-edge machine learning techniques tailored to work seamlessly with large-scale data warehouse systems.
- Collaborate: Work closely with engineering leaders and research scientists to bring new ideas to life and tackle real-world challenges.
- Deliver: Build and deploy production-quality systems that scale, ensuring the system is fast, reliable, and secure.
Your Foundation:
- Degree: Recent graduate with a BS in Computer Science, Engineering, or a related field. If you have an MS or Ph.D., that’s a bonus!
- Skills: Strong foundation in software development, systems design, and algorithms. Proficiency in programming languages like Python, Java, or C++.
- Passion: A deep interest in machine learning and cloud technologies. You’re excited about building scalable, high-performance systems.
- Curiosity: An eagerness to learn and grow in an innovative, fast-paced environment.
Your Extra Special Sauce:
- Experience with cloud platforms like AWS, Azure, or GCP.
- Knowledge of distributed systems and databases.
- Exposure to machine learning frameworks like PyTorch or TensorFlow.
- Hands-on experience building APIs or microservices.
- Contributions to open-source projects, research papers, or university-based ML work.
We are an equal opportunity employer and value diversity at our company. We do not discriminate on the basis of race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status.
Kumo Mountain View, California, USA Office
357 Castro St, Suite 200, Mountain View, CA, United States, 94041
What you need to know about the San Francisco Tech Scene
San Francisco and the surrounding Bay Area attracts more startup funding than any other region in the world. Home to Stanford University and UC Berkeley, leading VC firms and several of the world’s most valuable companies, the Bay Area is the place to go for anyone looking to make it big in the tech industry. That said, San Francisco has a lot to offer beyond technology thanks to a thriving art and music scene, excellent food and a short drive to several of the country’s most beautiful recreational areas.
Key Facts About San Francisco Tech
- Number of Tech Workers: 365,500; 13.9% of overall workforce (2024 CompTIA survey)
- Major Tech Employers: Google, Apple, Salesforce, Meta
- Key Industries: Artificial intelligence, cloud computing, fintech, consumer technology, software
- Funding Landscape: $50.5 billion in venture capital funding in 2024 (Pitchbook)
- Notable Investors: Sequoia Capital, Andreessen Horowitz, Bessemer Venture Partners, Greylock Partners, Khosla Ventures, Kleiner Perkins
- Research Centers and Universities: Stanford University; University of California, Berkeley; University of San Francisco; Santa Clara University; Ames Research Center; Center for AI Safety; California Institute for Regenerative Medicine
