Lead Machine Learning Engineer, Lifetime Value

This is a fully remote position, open to applicants in United States.

📋 Description

• Construct and enhance the systems that drive customer lifetime value modeling, encompassing development, deployment, monitoring, and production support.

• Collaborate with data scientists to transform statistical models, simulations, and forecasting workflows into production-ready solutions that facilitate business decision-making.

• Expedite the transition from research to production by implementing scalable infrastructure, dependable workflows, and reusable tools.

• Enhance the machine learning development experience by establishing improved operational patterns and advancing practices suitable for production-grade ML.

• Create tools and services that assist stakeholders in assessing model performance, comprehending business impact, and placing trust in model outputs during production.

• Work in partnership with technical and business collaborators to address high-value challenges and enhance the reliability and scalability of ML systems.

• Disseminate best practices through mentorship, documentation, and effective communication regarding technical decisions, trade-offs, and operational factors.

⛳️ Requirements

• Bachelor’s degree in Computer Science, Statistics, Mathematics, Engineering, or a related quantitative discipline.

• Over 5 years of experience in designing, constructing, deploying, and maintaining machine learning systems and ML model pipelines in collaboration with data scientists.

• Proficient in Python and software engineering principles, capable of developing maintainable ML systems and production-quality code.

• Experience in building and managing production ML systems, including deployment, monitoring, debugging, and workflow orchestration.

• Ability to design reproducible systems with definitive lineage, versioning, and operational visibility throughout complex ML workflows.

• Comfort with ML systems that feature interconnected components, simulation-driven logic, and embedded business rules.

• Strong judgment regarding model evaluation, code quality, system reliability, and sustainable engineering trade-offs.

• Familiarity with cloud-based ML infrastructure and data platforms such as AWS, GCP, or Azure.

• Experience with infrastructure as code tools, such as Terraform.

• Excellent communication skills, with the ability to articulate technical trade-offs to both technical and non-technical stakeholders.

🏝️ Benefits

• Eligible for Competitive Bonus & Equity Offering