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San Francisco, California, United States
Hybrid
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.
You'll build and maintain the platform that powers how the world's leading brands measure the true causal impact of their marketing spend. Our Science Platform runs geo-based experiments across 100+ customers, processes daily analysis pipelines, and delivers statistical results that directly drive budget decisions worth millions of dollars.
This is a backend and platform engineering role. You'll work primarily in Python across a set of tightly integrated repositories: a statistical estimation library, a science orchestration library, and a Metaflow-based job execution system running on Kubernetes. You'll collaborate closely with applied scientists to translate research into production code, and with product engineers to ensure results flow cleanly into the customer-facing application.
You won't be starting from a blank canvas — you'll be joining a production system that serves real customers and shipping improvements that compound. The engineers who thrive here are the ones who can navigate a complex, multi-repo codebase, understand the science well enough to be a productive partner, and ship reliable systems without needing to rewrite everything first.
We're also a team that leans into AI-assisted development as a genuine force multiplier. Our engineers use tools like Claude Code and Cursor to move faster, accelerate exploratory work, and ship features that would have taken weeks in days. We're looking for someone who's excited about this way of working.
Build and evolve the data pipelines that fetch, aggregate, and transform KPI data from BigQuery across multiple geographies and granularities
Extend and maintain the statistical estimation library — implement new estimators, improve standard error methods, and optimize performance for large panel datasets
Improve the Metaflow-based analysis orchestration system that schedules and executes thousands of daily experiment analyses on Kubernetes
Design for reliability: build monitoring, alerting, and self-healing patterns for pipelines that run autonomously every day
Collaborate closely with applied scientists to translate research prototypes into production-grade code with proper testing, error handling, and observability
Work with product engineers to ensure analysis results are published correctly and flow cleanly into the customer-facing API and frontend
Use AI development tools as part of your daily workflow to accelerate delivery and explore solutions
Participate in on-call rotation and own the operational health of the science platform systems
3+ years of experience building and shipping production software systems
Must have strong Python proficiency — you write clean, well-tested Python and are comfortable with the ecosystem (pandas, numpy, pytest, poetry)
Experience with data-intensive applications: you've worked with large datasets, data pipelines, or ETL systems and understand the tradeoffs
Experience with SQL and analytical databases (BigQuery, Snowflake, or similar) — you can write performant queries and understand how warehouse-scale data processing works
Comfort with cloud-native environments (GCP preferred): you understand how to deploy, monitor, and operate services in production
Experience with workflow orchestration frameworks (Metaflow, Airflow, Dagster, Prefect, or similar) is a strong plus
Track record of working effectively with AI development tools (Claude, Cursor, Copilot, or similar) — you've integrated them into your workflow and can articulate how they change the way you build software
Ability to collaborate productively with scientists and researchers — you don't need a PhD, but you should be comfortable reading statistical code, understanding experimental design concepts, and asking good questions
Excellent communication skills — you can explain technical tradeoffs clearly and work effectively across disciplines
Earlier stage startup experience
Familiarity with statistical or scientific computing (scipy, scikit-learn, Bayesian methods) — enough to be a productive partner to scientists
Experience with Kubernetes and containerized workloads
Experience with event-driven architectures (Pub/Sub, message queues)
Experience with experimentation platforms, A/B testing infrastructure, or causal inference systems
Experience working across multiple interconnected repositories with coordinated release cycles
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.
