Yellowbrick Data is a Silicon Valley based startup who invented a Kubernetes based cloud-native data platform for the hybrid multi-cloud world. Yellowbrick’s innovative platform can run in customer’s cloud accounts or on-premises private clouds, offering flexibility while ensuring data residency, localization, and sovereignty, all with significant performance improvements. Major insurers, credit card companies, telcos, healthcare firms and government agencies all trust Yellowbrick to transform data into actionable insights, quickly without compromising data security. Join us to drive business transformation at the forefront of data innovation.
We’re a driven R&D team, solving hard-core computer science problems at all levels of the stack. What we do isn’t easy: We design hardware, build an operating system, have created an entire enterprise-grade database from scratch with remote support capability, and actively build and maintain business continuity functions and a growing set of ecosystem integrations. Our database deploys both on-premises and in the cloud.
We are looking for a motivated software engineer to work in our core database team in areas such as query execution, performance optimization, cluster management, addition of semantic search capabilities and efficient storage of document embeddings. You'll have the opportunity to work in all areas of our software stack which includes almost every aspect of computer science - from hardware to operating systems and user interfaces and everything in between.
You’re a computer scientist who loves algorithms, massively parallel programming, multicore architecture and performance optimizations. You find compilers, parsers, grammars and compilers interesting, you find parallel sorting, aggregation, indexing and joining algorithms interesting, have a flair for creative solutions to generating more efficient machine instructions, and hopefully you’ve worked in one or more of these areas. You like Big Data algorithms that work at scale and concurrency, embrace massive parallelism of algorithms in distributed systems and don’t get scared by NP complete problems or dynamic programming.
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