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Database Technologies Jobs in San Francisco

73 open positions · Updated 3 months ago

Average salary: 186.8k–278.2k/yr

Showing 20 of 73 positions

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Gusto
Gusto Denver, CO;San Francisco, CA;New York, NY;Los Angeles, CA;Seattle, WA $200k–$230k/yr Published 6 months ago
Airtable
Airtable San Francisco, CA; New York, NY; Seattle, WA $187k–$260k/yr Published 5 months ago
Airtable
Airtable San Francisco, CA; New York, NY; Remote - US (Seattle, WA only) $196k–$339.9k/yr Published 5 months ago
Perplexity

Design and improve the storage platform for Perplexity's products and AI workloads, ensuring reliability and performance.

Perplexity San Francisco Published 1 month ago
Flexible on stack
Databricks

Join Databricks as a Lakebase Sales Specialist to drive adoption of our fully-managed Postgres offering for intelligent applications.

Databricks Remote - Georgia; Remote - Texas; San Francisco, California Published 1 month ago
Anthropic

Join Anthropic as a Staff+ Software Engineer to build and scale the database infrastructure for AI systems.

Anthropic San Francisco, CA | New York City, NY | Seattle, WA $320k–$485k/yr Published 3 weeks ago
Flexible on stack
LangChain

Lead the SmithDB team at LangChain, focusing on hands-on engineering and team management in a fast-paced environment.

LangChain San Francisco, CA $215k–$260k/yr Published 4 weeks ago
Flexible on stack Heavy meetings
Airtable
Airtable San Francisco, CA; New York, NY $213k–$339.9k/yr Published 3 months ago
PlanetScale

Join PlanetScale as a Software Engineer to build Neki, a sharded PostgreSQL product, and tackle complex distributed systems challenges.

PlanetScale San Francisco Bay Area or Remote $120k–$290k/yr Published 1 year ago
Databricks
Databricks Mountain View, California; San Francisco, California $190k–$261.3k/yr Published 4 months ago
PlanetScale

Join PlanetScale as a Software Engineer to design and build the control plane for Neki, a sharded PostgreSQL product.

PlanetScale San Francisco Bay Area or Remote $120k–$290k/yr Published 1 month ago
Flexible on stack
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