Staff Infrastructure Engineer
Espresso AI
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Benefits offered by this job
Job summary
Qualifications
- 7+ years software engineering experience.
- Strong fundamentals in systems design, performance engineering, and debugging.
- Ability to own complex technical projects end-to-end.
Responsibilities
- Design, build, and scale distributed execution and scheduling systems.
- Develop internal compute environments to safely offload workloads.
- Implement compiler-style analysis and optimization of SQL workloads.
- Build infrastructure and data pipelines that support ML modeling.
- Improve performance, reliability, and observability across the platform.
- Influence architectural decisions and contribute to engineering strategy.
- Mentor junior engineers as we scale the team.
Skills
Job description
Join the Espresso AI team as a Staff Infrastructure Engineer. Espresso AI’s mission is to use machine learning to automate performance engineering, helping customers reduce Snowflake and Databricks SQL compute costs by up to 70%.
Espresso AI is a well‑funded early‑stage startup scaling quickly. Our next‑generation, AI‑based approach saves users huge amounts of money by reducing costs of massive data analytics workloads. We recently raised a round led by Nat Friedman and Daniel Gross.
As a Staff Infrastructure Engineer, you will design and build the distributed systems that power Espresso’s optimization engine. You will work across real‑time scheduling, workload execution, performance analysis, and the internal compute environments that allow us to safely and efficiently offload warehouse workloads. You will own major architectural components, drive reliability and performance improvements, and collaborate closely with founders and engineering on long‑term technical direction.
- Design, build, and scale Espresso’s distributed execution and scheduling systems
- Develop internal compute environments to safely offload workloads from Snowflake and Databricks
- Implement compiler‑style analysis and optimization of SQL workloads
- Build infrastructure and data pipelines that support ML modeling and product features
- Improve performance, reliability, and observability across the platform
- Influence architectural decisions and contribute to engineering strategy as an early team member
- Mentor junior engineers as we scale the team, fostering strong engineering fundamentals and high‑quality execution
- 7+ years software engineering experience
- Strong fundamentals in systems design, performance engineering, and debugging
- Ability to own complex technical projects end‑to‑end
- Comfort operating across a broad range of infrastructure challenges and tackling whichever problems are most critical to the system
- Experience at small startups
- Background in data warehouse cost optimization
- Experience with workload orchestration, query execution, scheduling, or runtime systems is a bonus
- Exposure to SQL optimization
- Experience with software verification
- Competitive salary and meaningful equity based on final leveling
- $225,000 - $300,000 Base Pay + Equity
- Employee‑friendly equity terms (early exercise)
- Health, dental, and vision insurance
- 401k with 4% match
- Free salads & gym membership
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