Machine Learning - Query Optimization Architect
Hybrid · San Mateo, California, United States
Job Summary
Role focused on building a next-generation analytics engine with a data-learning optimizer to deliver 10x-100x runtime speed-ups for cloud data warehouses. As part of the founding team, contribute to path-breaking innovation in ML-driven query optimization, collaborating across researchers and engineers to ship high-value cloud data products. Key tech focus includes cost-based query optimization, relational databases, NoSQL, data lakes, and OLAP, with strong coding in Java and Python, Git workflows, and open-source collaboration. Desirable experience in cloud file systems, in-memory databases, BI tools, and DevOps on AWS/GCP/Azure. The role emphasizes rapid iteration, startup pace, cross-functional teamwork, and ownership of impactful data-product outcomes. H1B sponsorship is available.
Required Qualifications
- 5+ years of experience in building cost-based query optimizers
- Solid experience in delivering high-value cloud data products over several product iterations
- Deep experience with relational databases, NoSQL and data lakes
- Familiarity with OLAP databases
- Strong background in Java, Python
- Familiar with the Git workflow, open-source projects on Github
- Experience and passion for building complex technological solutions in a fast-paced and unstructured start-up environment
- Comfortable working quickly, with lots of iterations, and shipping often
- Excellent communication, collaboration, and interpersonal skills: empathy, listening, teamwork, and a great sense of responsibility
- Willingness to learn and take on new challenges every day
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