Senior Machine Learning Engineer

Own machine learning initiatives end to end, from problem formulation and offline evaluation to production deployment and online A/B testing, driving measurable improvements in user engagement, retention, and trading activity.

Build and iterate on multi-stage recommendation, retrieval, and contextual bandit models that power personalized experiences for millions of users across crypto, equities, derivatives, and prediction markets.

Design intelligent notification orchestration, candidate ranking, and targeting systems that deliver timely, high-value alerts while minimizing user fatigue.

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, reducing technical debt, leading incident response, and improving operational reliability.

Partner with Product, Data Science, and Engineering to shape the team roadmap, mentor L3-L4 engineers, and build AI-native workflows and automation that increase team velocity.

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 experience 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 the design and 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.

Experience using generative AI responsibly, with human oversight, to deliver business-ready outputs and drive measurable improvements in workflow efficiency, cost, and quality.