---
title: 'Staff Infrastructure Engineer at Espresso AI'
canonical: 'https://feeny.ai/job/staff-infrastructure-engineer-espresso-ai-new-york-j1wvad7qd85j'
type: 'job'
last_seen: '2026-09-11'
---

# Staff Infrastructure Engineer at Espresso AI

- **Company:** Espresso AI
- **Location:** New York, NY
- **Compensation:** $225k–$450k
- **Employment:** full-time
- **Work type:** onsite
- **Posted:** 2024-09-17
- **Last confirmed live:** 2026-09-11
- **Apply:** https://jobs.ashbyhq.com/espresso/26a0e977-4d76-4172-b6de-1226cde1562f

## Job description

## About Espresso AI

Espresso AI’s mission is to use machine learning to automate performance engineering. Today, we help our customers reduce Snowflake and Databricks SQL compute costs by up to 70%, unlocking massive efficiency gains without requiring any workflow changes.

More and more businesses are adopting data warehouses to manage invaluable data analytics workloads. Unfortunately, the costs of these workloads often grow exponentially over time, with no easy way for users to reduce cost — until now. Our next-generation, AI-based approach saves our users huge amounts of money.

We’re a well-funded, early-stage startup scaling quickly. Our most recent round was led by Nat Friedman and Daniel Gross.

## About the Role

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. This is a highly technical role with deep systems work at its core.

## What You’ll Work On

- 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

## What You Bring

- 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

Nice to Haves

- 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

## What We Offer

- 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

## About Espresso AI

## Company Overview
- **One-liner**: Espresso AI uses generative AI and neural optimization to automatically reduce cloud data warehouse costs for Snowflake and Databricks, saving customers up to 70% on their compute bills.
- **Entity Type**: Private (Seed stage)
- **Headquarters**: Brooklyn, New York, USA
- **Founded**: 2023
- **Founders**: Ben Lerner (CEO & Co-Founder), Alex Kouzemtchenko (CTO & Co-Founder), Juri Ganitkevitch (Co-Founder)

## Core Business
- **Primary Industry**: Cloud cost optimization / AI-powered data infrastructure
- **Target Customers**: B2B — enterprises and SMBs using Snowflake and Databricks who face rising compute costs
- **Mission**: To make AI accelerate compute by 1000x, starting with data warehousing, by building the world’s first neural optimizer.

## Products & Services
- **Espresso AI Neural Optimizer (Core Product)**: An automated, ML-driven platform that optimizes Snowflake and Databricks data warehouses in real-time. The system runs autonomously, applying performance engineering and scheduling optimizations to cut compute costs by up to 70%. Set-up requires a single SQL command.
- **Kubernetes for Snowflake**: A product launched in 2025 that uses Kubernetes to renovate and optimize Snowflake data warehouses, further reducing costs and improving performance.

## Market Standing
- **Valuation/Market Cap**: Not publicly available
- **Key Metric**: Total funding of $11.0M (Seed round, led by Nat Friedman and Daniel Gross, announced May 2024)
- **Notable Investors/Partners**: Nat Friedman, Daniel Gross, FirstMark Capital (Matt Turck), and angel investors including Tristan Handy (dbt Labs founder)
- **Growth Signals**: 
  - 15 employees (+35.7% YoY)
  - Multiple AEs actively closing deals — the company states they have product-market fit
  - Expanding support from Snowflake-only to include Databricks
  - Strong inbound interest and active hiring for engineering and sales roles

## Competitive Advantages
- **First Neural Optimizer**: Espresso AI is building what it calls the world’s first neural optimizer — using LLMs to deeply understand and optimize SQL code, scheduling, and infrastructure, leapfrogging traditional rule-based optimization tools.
- **Elite Team**: Founders and engineers come from Google (NLU for Search), Apple (Siri), and MIT, with deep expertise in performance engineering and AI.
- **Guaranteed ROI Model**: Only charges customers based on savings — no onboarding costs, no minimums, no commitments — creating strong alignment and trust.
- **Autonomous 24/7 Operation**: Unlike hiring a team of data engineers, the platform runs continuously, requiring minimal human intervention after setup.

## Strategic Focus
- **Near-term**: Deepen Snowflake and Databricks optimization capabilities; scale sales team to capture growing demand; hire staff-level engineers to build out distributed systems and ML infrastructure.
- **Long-term**: Expand beyond data warehousing to optimize all major sources of cloud compute, making AI-driven compute optimization a universal infrastructure layer.

## Why Work Here
- **High Hiring Bar & Elite Peers**: The company explicitly targets “the strongest group of engineers you’ve ever worked with.” Staff engineers from Google, Apple, and MIT form the core team.
- **Early-Stage Impact**: With ~15 people and strong product-market fit, new hires get substantial equity and can significantly shape the company’s trajectory. New engineers are expected to ship a meaningful feature in their first week.
- **Compensation**: Competitive salary + meaningful equity. Engineering roles: $225,000–$450,000 salary + equity. Sales (OTE): $200,000–$350,000.
- **Office Culture**: 5 days/week in-office in Brooklyn, NYC. Relocation support is available.
- **Autonomy & Ownership**: The company values engineers who work autonomously, prioritize ruthlessly, and communicate effectively. Sales hires are expected to help build the sales process from the ground up.
- **Cutting-Edge Work**: Engineers train cutting-edge models that answer questions like “how fast will this code run?” and “can this SQL query run faster on larger infra?” — blending AI, distributed systems, and compiler-style optimization.

## Sources
1. [espresso.ai](https://espresso.ai/)
2. [espresso.ai/about](https://espresso.ai/about)
3. [espresso.ai/careers](https://espresso.ai/careers)
4. [LinkedIn Company Page](https://www.linkedin.com/company/espresso-ai)
5. [VentureBeat](https://venturebeat.com/) (Espresso AI emerges from stealth with $11M to tackle the data cloud cost crisis, 2024-05-25)
6. [Jobs.ashbyhq.com/espresso](https://jobs.ashbyhq.com/espresso)

## Other roles at Espresso AI

- [Staff ML Engineer](https://feeny.ai/job/staff-ml-engineer-espresso-ai-new-york-fw6k9dd1beef) — New York, NY
- [Staff Infrastructure Engineer](https://feeny.ai/job/staff-infrastructure-engineer-polymarket-new-york-8z4ae58njtbe) — New York, NY
- [Staff Infrastructure Engineer](https://feeny.ai/job/staff-infrastructure-engineer-vgs-united-states-xbftp83dtw52) — United States
- [Staff Infrastructure Engineer](https://feeny.ai/job/staff-infrastructure-engineer-komodo-health-united-states-n630nvv2w4er) — United States
- [Staff Infrastructure Engineer](https://feeny.ai/job/staff-infrastructure-engineer-kikoff-san-francisco-3260rkkcw562) — San Francisco, CA
- [Staff Infrastructure Engineer](https://feeny.ai/job/staff-infrastructure-engineer-headway-remote-pn98hk39zend)
- [Staff Infrastructure Engineer](https://feeny.ai/job/staff-infrastructure-engineer-ivo-inc-san-francisco-8ry13p18017s) — San Francisco, CA
- [Staff Infrastructure Engineer](https://feeny.ai/job/staff-infrastructure-engineer-aurelian-seattle-fvkea1041kfg) — Seattle, WA
- [Staff Infrastructure Engineer](https://feeny.ai/job/staff-infrastructure-engineer-fanduel-atlanta-georgia-c4y8nxyaqhjk) — Atlanta Georgia, United States
- [Staff Infrastructure Engineer](https://feeny.ai/job/staff-infrastructure-engineer-fanduel-new-york-v4c714gw53mv) — New York, NY