Senior Machine Learning Engineer

Overview

Senior Machine Learning Engineer (L5) on the ML & Discovery team in Consumer Growth, building machine learning systems that help customers discover what to trade and engage with next across crypto, equities, derivatives, and prediction markets.

What you'll do

• Own end-to-end machine learning initiatives from problem formulation and offline evaluation to production deployment and online A/B testing, driving measurable lifts in user engagement, retention, and trading activity.
• Build and iterate on multi-stage recommendation, retrieval, and contextual bandit models that serve personalized experiences to millions of users across crypto, equities, derivatives, and prediction markets.
• Design intelligent notification orchestration, candidate ranking, and targeting systems that match users with timely, high-value alerts while avoiding fatigue and improving user experience.
• Develop production LLM pipelines, grounding workflows, and automated evaluation loops that generate accurate, real-time market insights and personalized financial content.
• Architect resilient, low-latency model serving services and real-time feature pipelines, eliminating technical debt, leading incident response, and raising our operational reliability bar.
• Partner with Product, Data Science, and Engineering to shape the team roadmap, mentor L3-L4 engineers, and build AI-native workflows and automation that multiply team velocity.

What you'll need

• 5+ years of experience in machine learning engineering and software development, with a track record of shipping production ML systems that move customer and business metrics.
• Proven track record in one or more relevant domains: recommendation systems, multi-stage ranking and retrieval, contextual bandits, notification targeting, or production LLM applications.
• Strong software engineering and data skills in a backend language such as Python or Go, with hands-on proficiency in modern ML and data stacks (such as PyTorch, GBDTs, Spark/Databricks, Ray, Kafka, and/or Tecton).
• Deep practical knowledge of offline model evaluation, training-serving skew mitigation, and design/interpretation of online A/B experiments tied to business metrics.
• Demonstrated ability to drive complex projects end-to-end, make pragmatic architectural trade-offs, and mentor junior engineers.
• Utilizes generative AI responsibly, maintaining human oversight to deliver business-ready outputs and drive measurable improvements in workflow efficiency, cost, and quality.

Details

• Remote-first, but not remote-only; expected to get together quarterly for in-person working sessions called "surges."
• Annual base salary range (excluding equity and bonus): $213,750 — $225,000 USD.