---
title: 'Staff Machine Learning Engineer - San Francisco at Haus Analytics'
canonical: 'https://feeny.ai/job/staff-machine-learning-engineer-san-francisco-haus-analytics-san-francisco-12v9ktej0ey5'
type: 'job'
last_seen: '2026-09-06'
---

# Staff Machine Learning Engineer - San Francisco at Haus Analytics

- **Company:** Haus Analytics
- **Location:** San Francisco, CA
- **Employment:** full-time
- **Posted:** 2026-08-20
- **Last confirmed live:** 2026-09-06
- **Apply:** https://jobs.ashbyhq.com/haus/1bf9bbcb-f525-43b6-9063-8541e043eec3

## Job description

## About Haus

Haus is the causal marketing platform top businesses trust to optimize billions in ad spend worldwide. With support from PhD economists, data scientists, and growth experts, Haus’ AI-driven technology translates complex marketing measurement into clear action and outcomes, enabling brands like Dyson, Wayfair, Sonos, Fanduel, SharkNinja, and Intuit to optimize spend, accelerate growth, and make smarter marketing decisions at scale.

## THE ROLE

This role will drive high-impact projects for advanced marketing planning, analysis, and optimization at Haus using optimization, machine learning, and causal inference. We are looking for individuals who not only excel in problem solving and critical thinking, but also are interested and proficient in writing production code, turning ideas to scalable systems. This role specifically will work deeply on the cMMM machine learning problem space. The role will be a blend of working with applied scientists, data scientists, data engineers and other MLEs to deliver trustworthy results to our customers while focusing on creating processes that help scale the business.

## WHAT YOU’LL DO

- Drive initiatives from concept to final product delivery, ensuring seamless end-to-end execution: lead or contribute to the design, development, optimization, and product ionization of machine learning (ML) solutions for complex and high-impact problems.
- Able to implement probabilistic techniques into reusable statistical libraries, including bootstrapping, statistical tests, and ML models/regressions.
- Build and maintain the ML systems that power Haus’ product lines (specifically cMMM).
- Review code and designs of teammates, providing constructive feedback.
- Lead and collaborate with engineering and cross-functional partners across product, engineering, and science teams to drive system development from ideation to production.
- Drive design and implementation of AI (Agentic) workflows for ML pipelines (including model validation)
- Mentor ML engineers and raise the organization’s ML bar

## QUALIFICATIONS

- PhD or equivalent experience in Computer Science, Engineering, Mathematics or related field
- 10+ years of industry experience ideally with a focus on Machine Learning Engineer, building and operating production ML systems.
- Experience in exploratory data analysis, statistical modeling, hypothesis testing, and experimental design.
- Experience working with cross-functional teams (product, science, product ops etc).
- Proficiency in one or more object-oriented programming languages (e.g. Python, Go, Java, C++).

## BONUS POINTS

- Experience in modern deep learning architectures and probabilistic modeling.
- Expertise in the design and architecture of ML systems and workflows.
- Experience with optimization techniques, including reinforcement learning (RL), Bayesian methods, and multi-armed bandits.
- Experience with MLFlow
- Experience with data science or machine learning approaches in marketing and growth

## WHAT WE OFFER:

We’re a high-performance, low-ego team operating in a fast-moving environment. We care deeply about our customers and expect everyone to take full ownership of their work — this is a place where high expectations fuel even higher growth.

If you thrive in ambiguity, take pride in raising the bar, and want to work alongside top-tier peers who challenge and support you, you'll find unmatched opportunities here. If you're looking for predictability or rigid structure or you prefer order-taking to go-getting, we’re probably not the right fit — and that’s okay.

We work in small, mission-driven teams that prioritize inclusion, collaboration, and growth over hierarchy or red tape.

Some of our benefits include:

- Flexible PTO - take time when you need it!
- Equity – Startup environment with part-ownership in our successes
- Top of the line health, dental, and vision insurance - multiple plan options so you can pick what fits you best
- WFH stipend to support the set up you need to be productive
- Events & Offsites – opportunities to connect and celebrate in real life!
- Free Lunch – Grab a bite on us when you choose to work from the office (hub locations include SF, NYC and Seattle)
- New Parent Leave – take time to welcome your newest Hausmate

We value in-person collaboration at Haus and give preference to candidates within commuting distance of our offices in San Francisco, Seattle, and New York City.

Haus is an equal opportunity employer. We make hiring decisions without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, age, disability, veteran status, or any other protected status.

We believe diverse perspectives make us stronger and are committed to an inclusive culture where everyone feels seen, heard, and empowered to contribute. Bring your authentic self — we would love to hear from you.

## About Haus Analytics

## Company Overview
- **One-liner**: Haus Analytics provides an AI-powered causal marketing measurement platform that helps brands measure the true incremental impact of their ad spend through experiments and daily attribution.
- **Entity Type**: Private (startup, latest funding round: additional $20M in 2024; total raised $57.15M)
- **Headquarters**: San Francisco, California, United States
- **Founded**: 2021
- **Founders**: Zach Epstein (CEO)

## Core Business
- **Industry**: Marketing analytics / Advertising technology / Decision science
- **Target Customers**: B2B – enterprise and growth-stage brands including Intuit, Hims & Hers, Pernod Ricard USA, Coursera, Bally Sports, Caraway, and Sonos
- **Mission**: “Democratize access to world-class decision science tools” and transform how businesses make marketing investment decisions.

## Products & Services
- **GeoLift**: Self-service incrementality testing platform that runs scientifically sound test/control geo experiments to measure the causal impact of advertising.
- **Causal Attribution**: Daily incrementality reporting that de‑biases legacy attribution tools and provides daily insights on the incremental impact of marketing across channels and tactics.
- **Haus Copilot**: AI‑powered assistant that designs, optimizes, and analyzes experiments from hypothesis through post‑treatment window.

## Market Standing
- **Valuation / Market Cap**: Not publicly disclosed
- **Key Metric**: Total funding raised $57.15M (Series unknown, latest $20M closed in mid‑2024)
- **Notable Investors & Partners**: 01 Advisors (David Fischer, Dick Costolo, Adam Bain), Insight Partners, DST Global, Gokul Rajaram, Baseline Ventures, Haystack Ventures, Octave, Mantis Venture Capital
- **Growth Signals**:
  - Customers collectively represent over $1 trillion in combined revenue.
  - Haus has helped optimize more than $30 billion in ad spend.
  - Runs over 4,000 experiments per year.
  - One customer (Newton Living) reported a >10x ROI on their annual Haus investment in the first two months.

## Competitive Advantages
- Combines rigorous causal inference (econometrics, statistics, AI) with an easy‑to‑use SaaS platform.
- Privacy‑durable methodology that works in a cookieless, IDFA‑limited world.
- GeoLift and Causal Attribution are patent‑pended or proprietary approaches that few competitors offer at scale.
- Team includes former leaders from Google, Netflix, Amazon, and other top tech companies.

## Strategic Focus
- Deepen self‑service experimentation and daily causal attribution capabilities.
- Expand into adjacent industries beyond advertising (e.g., pricing, product decisions) and broader decision science.
- Continue to build AI‑powered Copilot features to reduce the need for dedicated data science teams.

## Why Work Here
- **Culture**: “Experiment boldly,” “Done is better than perfect,” “Call it what it is” – values that encourage risk‑taking, candor, and speed.
- **Work Model**: Hub‑centered hybrid with offices in San Francisco, New York, and Seattle. Many roles are hybrid or location‑anchored; remote employees get regular team onsites and travel support.
- **Notable Perks**: Equity, healthcare (medical, dental, vision), flexible PTO, new parent leave, WFH stipend, free in‑office lunch, brand discounts, events and offsites.
- **Engineering & Science Culture**: Built by scientists and engineers; the team applies economics, ML, and data pipeline expertise to solve hard marketing measurement problems.

## Sources
1. [haus.io – Homepage](https://www.haus.io/)
2. [haus.io – About / Leadership](https://www.haus.io/about)
3. [haus.io – Careers](https://www.haus.io/careers)
4. [cbinsights.com – Haus Analytics Profile](https://www.cbinsights.com/company/haus-analytics)
5. [ashbyhq.com – Haus Careers](https://jobs.ashbyhq.com/haus)

## Other roles at Haus Analytics

- [Senior Software Engineer - Data Onboarding - Seattle](https://feeny.ai/job/senior-software-engineer-data-onboarding-seattle-haus-analytics-seattle-dg5dfwnrxprj) — Seattle, WA
- [Senior Software Engineer - Data Onboarding - San Francisco](https://feeny.ai/job/senior-software-engineer-data-onboarding-san-francisco-haus-analytics-san-e01842gf9cf3) — San Francisco, CA
- [Director of Demand Generation](https://feeny.ai/job/director-of-demand-generation-haus-analytics-remote-qkzh9vrhyx7s)
- [Staff Backend Engineer - Data Platform- Seattle](https://feeny.ai/job/staff-backend-engineer-data-platform-seattle-haus-analytics-seattle-vastxze0k4hz) — Seattle, WA
- [Staff Backend Engineer - Data Platform- San Francisco](https://feeny.ai/job/staff-backend-engineer-data-platform-san-francisco-haus-analytics-san-francisco-m34j11anw5df) — San Francisco, CA
- [Staff Machine Learning Engineer - New York](https://feeny.ai/job/staff-machine-learning-engineer-new-york-haus-analytics-new-york-98726n18ebs1) — New York, NY
- [Staff Machine Learning Engineer - Seattle](https://feeny.ai/job/staff-machine-learning-engineer-seattle-haus-analytics-seattle-kkcb4ah5z2sf) — Seattle, WA
- [Marketing Measurement Specialist (MMM) - Seattle](https://feeny.ai/job/marketing-measurement-specialist-mmm-seattle-haus-analytics-seattle-c0vhymzry39r) — Seattle, WA
- [Marketing Measurement Specialist (MMM) - New York](https://feeny.ai/job/marketing-measurement-specialist-mmm-new-york-haus-analytics-new-york-3h9kmavpw2k0) — New York, NY
- [Marketing Measurement Specialist (MMM) - San Francisco](https://feeny.ai/job/marketing-measurement-specialist-mmm-san-francisco-haus-analytics-san-francisco-rcrwsgjby829) — San Francisco, CA