Staff Machine Learning Engineer, Pricing Platform

• Define the long-term technical roadmap that accelerates pricing innovation through ML tools and workflows that improve the end-to-end pricing R&D process, balancing iterative delivery with the long-term vision for the platform
• Work closely with researchers to define platform needs that improve R&D ergonomics from data readiness through feature engineering, model fitting, serving, diagnostics, and monitoring
• Define the architecture and versioned contracts connecting data, features, models, and production pricing, ensuring the platform remains reproducible and technically coherent as it evolves
• Automate end-to-end workflows, leveraging LLM technology to power agentic data science workflow automation
• Write and review code in the most critical parts of the platform and drive technical design across systems and cross-team dependencies
• Mentor senior engineers in architecture, platform design, and best practices in ML engineering
• Set standards for reliability, observability, reproducibility, and correctness across the platform’s systems

• →10+ years of software engineering experience, with a demonstrated track record of designing and delivering business-critical ML platforms, data platforms, or similarly complex distributed systems
• →Demonstrated architectural ownership of production ML infrastructure, including systems such as feature pipelines or feature stores, model training and orchestration, model registries and versioning, model serving, or research-to-production infrastructure
• →Strong system design and distributed systems expertise, including experience designing reliable, scalable data processing systems and well-defined interfaces between complex systems
• →Experience designing systems that provide strong guarantees around reproducibility, lineage, versioning, training-serving consistency, and production correctness
• →Working knowledge of the ML lifecycle and the engineering considerations involved in training, evaluating, deploying, and operating models in production
• →Demonstrated ability to establish technical direction in ambiguous problem spaces and translate long-term architectural goals into incremental, executable plans
• →A track record of creating technical leverage across multiple teams through shared platforms, abstractions, standards, or tooling
• →Demonstrated ability to influence technical direction across teams without direct authority and mentor senior engineers on architecture and system design
• →Strong experience collaborating with Data Scientists and researchers to understand research workflows and translate their needs into scalable platform capabilities
• →Proficiency with Python and modern ML and data tooling
• →Excellent written and verbal communication skills, with the ability to communicate effectively across Engineering, Data Science, Product, and leadership

• Understanding of ML and statistical modeling, including model assumptions, bias and variance, uncertainty, evaluation methodology, and common model failure modes
• Understanding of how common ML algorithms and frameworks operate beneath their interfaces and how those details influence production system design
• Experience improving model development velocity, experimentation throughput, deployment reliability, or model quality through ML-platform investments
• Experience building ML infrastructure in a regulated or highly data-intensive domain such as insurance, fintech, financial services, or healthcare
• Experience designing platforms used by quantitative researchers or Data Scientists with demanding experimentation and reproducibility requirements
• Experience applying LLMs or agentic systems to developer tooling, research workflows, or data science automation

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