---
title: 'Staff ML Engineer at Espresso AI'
canonical: 'https://feeny.ai/job/staff-ml-engineer-espresso-ai-new-york-fw6k9dd1beef'
type: 'job'
last_seen: '2026-09-11'
---

# Staff ML 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/db3c6bde-5ff8-4234-9425-1ee8cf93c97e

## Job description

Train cutting-edge models to optimize our users' compute costs.

## 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 Infrastructure Engineer](https://feeny.ai/job/staff-infrastructure-engineer-espresso-ai-new-york-j1wvad7qd85j) — New York, NY
- [Staff ML Engineer](https://feeny.ai/job/staff-ml-engineer-via-tel-aviv-k4xmsz04gsm1) — Tel Aviv, Israel
- [Staff ML Engineer](https://feeny.ai/job/staff-ml-engineer-cloudbeds-europe-27rk5r1v7dnj) — Europe
- [Staff ML Engineer](https://feeny.ai/job/staff-ml-engineer-docker-palo-alto-xq1a83mtzxe6) — Palo Alto, CA
- [Staff ML Engineer](https://feeny.ai/job/staff-ml-engineer-yubo-paris-axbfm7778gh8) — Paris, France
- [Staff ML Engineer](https://feeny.ai/job/staff-ml-engineer-typeface-palo-alto-z51mfr8xvjtw) — Palo Alto, CA