Staff ML Engineer: Production ML Systems & Causal Analytics

Haus; New York (NY); Hybrid; USD 150,000 - 230,000; Full time

Benefits offered by this job

Flexible PTO

Equity

Health insurance

WFH stipend

Events & Offsites

Free Lunch

New Parent Leave

Job summary

Haus seeks an experienced Machine Learning Engineer to drive high-impact marketing analytics projects with optimization, ML, and causal inference. You will write production-ready code and partner with applied scientists, data engineers, and engineers to deliver scalable, trustworthy results.

The role emphasizes building ML systems (including cMMM), mentoring engineers, and advancing AI workflows while collaborating across product and science teams.

Qualifications

• PhD or equivalent in CS, Engineering or Math.
• 10+ years of industry experience, focused on ML engineering and production systems.
• Experience with exploratory data analysis, statistical modeling, hypothesis testing, and experimental design.
• Experience working with cross-functional teams (product, science, product ops, etc.).

Responsibilities

• Lead design, development, optimization, and productionization of ML solutions for high-impact problems.
• Implement probabilistic techniques into reusable statistical libraries and ML models.
• Build and maintain ML systems powering Haus products (e.g., cMMM).
• Review teammates' code and designs, providing constructive feedback.
• Collaborate with engineering, product, and science teams to drive system development from ideation to production.
• Design and implement AI workflows for ML pipelines (model validation).
• Mentor ML engineers and raise the organization’s ML bar.

Skills

Machine Learning

Production ML systems

Python

Go

Java

C++

Education

PhD or equivalent in CS/Engineering/Math

Tools

Python

Go

Java

C++

Job description

Haus seeks an experienced Machine Learning Engineer to drive high-impact marketing analytics projects with optimization, ML, and causal inference. You will write production-ready code and partner with applied scientists, data engineers, and engineers to deliver scalable, trustworthy results.

The role emphasizes building ML systems (including cMMM), mentoring engineers, and advancing AI workflows while collaborating across product and science teams.