Amplitude
Staff Software Engineer, Business Systems & Data
$198K–$331K
San Francisco · Staff · Full-time · Posted Sep 21, 2026
- Experience
- 5+ years engineering experience shipping and running production services.
- Education
- Bachelor's Degree in Computer Science, Engineering or related field.
About the job
Build AI-native full-stack applications, integrations, and data systems that improve internal business operations and financial reconciliation.
What you'll do
- Design and build full-stack apps and tools for operational use cases; own them across the software development lifecycle.
- Build service connectors for AI and automation tools, and evolve tools and processes to improve team velocity.
- Drive end-to-end financial reconciliation from quote to revenue recognition, supporting cross-system reporting and traceability.
What they ask for
- 5+ years engineering experience shipping and running production services.
- Bachelor's Degree in Computer Science, Engineering or related field.
- Full-stack development experience across frontend, backend, tools and integrations; experience with data warehouses and SQL-based systems.
- Experience with frontier AI tooling, including harnesses, MCP servers, and agents.
Nice to have
- Experience evaluating LLM systems using eval harnesses, regression suites, and accuracy tracking over time.
- Familiarity with dbt, Snowflake, medallion-style warehouse architecture, or NetSuite, Salesforce, Workday, Zip, Ramp, or Stripe; prior internal-tools, solutions, or platform engineering experience.
Pay
$198,000 - $299,000 USD total target cash (inclusive of bonus or commission); $220,000 - $331,000 USD total target cash (inclusive of bonus or commission); $220,000 - $331,000 USD total target cash (inclusive of bonus or commission); $198,000 - $299,000 USD total target cash (inclusive of bonus or commission); $198,000 - $299,000 USD total target cash (inclusive of bonus or commission)
Tools
SQL, MCP servers, dbt, Snowflake, NetSuite, Salesforce, Workday, Stripe
AI focus
Build AI-native apps, agents, tools, and service connectors; apply frontier AI tooling to internal systems and workflows; optionally evaluate LLM systems.
Model-written summary · the original posting is authoritative