T

turbopuffer

Serverless vector and text search database

Database Engineer

Backend EngineeringConfirmed live in the last 24 hours
Full-timeNo salary listedRemote

About the Job

Requirements

  • Extensive experience working in a systems programming language such as Rust, C++, Java, or Go.
  • Ability to reason about memory layouts, cache lines, and system calls.
  • Ability to methodically investigate problems to identify root causes.
  • Expertise in at least one of search internals, storage engines, or distributed systems.
  • Comfort working in Linux terminals and using eBPF, perf, strace, and gdb.
  • Ability to write clearly and explain complex ideas simply.
  • Ability to build trust and acknowledge uncertainty.

Responsibilities

  • Design and implement improvements across one or more layers of the search engine, including storage, indexing, and querying.
  • Improve system performance through tracing, CPU and heap profiling, optimizing data structures, using SIMD, and tuning I/O pipelines.
  • Evolve search primitives and APIs for full-text search, vector search, and reranking.
  • Work closely with customers to support their workloads and incorporate learnings into the product.
  • Write clear documentation.

Desired Qualifications

  • Multiple years of experience writing Rust.
  • Experience maintaining or operating production systems at scale.
  • Contributions to database, information retrieval, or systems communities through research, open source, or speaking.
  • Expertise in Kubernetes and cloud infrastructure, including GKE, EKS, AKS, disks, networking, load balancers, and observability.
  • Expertise in security, including multi-tenant systems, isolation, and sandboxing.

About the Company

turbopuffer

Serverless vector and text search database

Turbopuffer provides a serverless vector and full-text search database for AI apps. It uses a tiered storage system that places data in low-cost object storage as the primary store, with an NVMe cache and memory tier to keep hot data fast, and it automatically moves data between tiers based on usage patterns. This approach lowers costs versus in-memory vector databases, with clients reporting substantial savings and low latency for frequently accessed data. The company aims to make AI infrastructure affordable and scalable by offering cost-effective, serverless vector and search capabilities that optimize storage automatically.

Company size
11-50
Company stage
Not listed
Total funding
Not listed
Headquarters
Ottawa, Canada
Founded
2023

Recently Posted Jobs

Explore more opportunities at turbopuffer

See more jobs