Senior Machine Learning Engineer

Summary

Join Haus, a marketing science platform, and drive high-impact projects using optimization, machine learning, and causal inference. You will lead or contribute to the design, development, and optimization of machine learning solutions for complex problems. This role requires implementing probabilistic techniques, building and maintaining ML systems, and collaborating with cross-functional teams. The ideal candidate possesses a PhD or equivalent experience, 5+ years of industry experience in building and operating production ML systems, and proficiency in object-oriented programming. Haus offers a competitive salary and is an equal opportunity employer.

Requirements

• 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++)

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

Preferred Qualifications

• 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

Benefits

$180,000 - $240,000 a year