Staff Machine Learning Engineer - San Francisco

Haus Analytics•San Francisco, CA

3d•$250,000 - $270,000•Onsite

About The Position

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.

Requirements

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++).

Nice To Haves

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

Responsibilities

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