Haus
Los Alamitos, CA
51-200 employees
Measure incrementality and allocate budget efficiently with Haus - your marketing science & experimentation platform to maximize growth.
[Senior/Staff] Machine Learning Engineer
[Senior/Staff] Machine Learning Engineer
About Haus
Haus is a marketing science platform that helps brands measure and maximize the business impact of their marketing spend with scientific precision. Over $360B spent annually on paid advertising in the US alone, and the famous quote “half the money I spend on advertising is wasted; the trouble is I don't know which half” still rings true. Haus helps marketers identify which half, and re-allocate it to maximize growth.
Haus was built by a team of former product managers, economists, and engineers from Google, Netflix, Meta, and others to make high-quality decision science accessible to businesses of all sizes. By automating the heavy lifting of experiment design, data processing, and insights generation, we empower our customers to make more profitable, data-driven decisions. We hear our customers frequently rave about our product, for example "we've seen north of 10x ROI on our annual investment in Haus in the first 2 months alone.”
Haus is on a hypergrowth trajectory, well-capitalized, and backed by top-tier VCs including Insight Partners, Baseline Ventures, Haystack, and others. We're honored that Haus has once again been recognized and has made the list for 2025's exceptional startups!
What you'll do:
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.
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.
- 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.
Qualifications:
- PhD or equivalent experience in Computer Science, Engineering, Mathematics or related field
- 5+ years of industry experience as an Applied Scientist/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 have:
- 7+ years of industry experience in machine learning, including building and deploying ML models.
- 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 data science or machine learning approaches in marketing and growth
$180,000 - $240,000 a year
Salary ranges are determined by role and level, and within the range individual pay is determined by additional factors including job-related skills, experience, and relevant education or training. Please note that the compensation details listed in this job posting reflect the base salary only, and do not include equity or benefits.
Haus is an equal opportunity employer and makes employment decisions without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, veteran status, disability status, age, or any other status protected by law.