In this role working on search you will work at the intersection between quality and performance optimizing the high performance components that serve our indexes for large amounts of traffic working on retrieval infrastructure to improve relevance for users inside the constraints that come with a high throughput index serving infrastructure designing and implementing retrieval augmented generation that is fed by our search typical tasks encompass:* Designing features and systems that enable to perform retrieval on large token and embeddings-based indexes* Optimizing throughput of the queries analyzing how to let ranking engineers improve the relevance within the latency and budget envelope* Streamlining onboarding and experimentation experience to our search systems to empower other teams to more efficiently use our components and iterate faster on their relevance improvements* Improving data structures and algorithms to reduce the cost of serving large indexes
Proven experience with at least one of the following programming languages: Go Java C/C Rust
Strong background in computer science: algorithms and data structures
Phenomenal interpersonal skills is required; able to work independently as well as in a team
Experience with information retrieval ML applied to search
Exposure to the challenges of scalable backend infrastructure and performance and how to diagnose analyse and resolve them with knowledge of profiling debugging tracing tools
Proficiency with distributed computing platform and technologies such as AWS GCP Kubernetes MapReduce or similar
Experience designing and implementing large scale data pipelines
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