Staff Software Engineer (Machine Learning)

• Join our team as a Staff Software Engineer (Machine Learning) and take ownership of the technical direction for our ML stack.
• Sound reasoning about time in data: point-in-time correctness, label leakage, feature availability, and training/serving skew

Responsibilities

• You will be responsible for feature definition and computation, training data generation, training infrastructure, model serving, and production monitoring.
• You will also build tooling for training/serving consistency, design model linkage to datasets, and enable data scientists to generate reproducible datasets.
• Additionally, you will measure delivery time, engineering effort, and rework, and use that evidence to prioritize improvements.
• In your first year, you will establish a delivery baseline, extend capabilities to additional models, standardize model monitoring, evaluate build-versus-buy options for ML platform tooling, and partner with engineers, data scientists, and analysts.
• Assumer la direction technique de l'ensemble de la pile ML, de la définition des fonctionnalités à la surveillance de la production.
• Construire des outils pour garantir la cohérence entre l'entraînement et le service, en tenant compte des différences de sources de données.
• Concevoir comment chaque modèle déployé reste lié à son ensemble de données, aux versions des fonctionnalités, aux étiquettes et au code d'entraînement.
• Experience establishing engineering standards, mentoring engineers, and helping teams adopt shared infrastructure
• Strong communication skills, with the ability to explain trade-offs clearly and find workable solutions across engineering, data science, risk, marketing, and finance

Requirements

• 8+ years of overall software engineering experience, including strong production skills in Python and SQL, experience building production systems in a JVM language, and 3+ years of experience building and maintaining ML platforms
• Feature store, feature platform, or ML platform experience at a company where models make consequential decisions is a plus
• Experience with streaming and change data capture, large-scale batch on Apache Beam or Spark, or distributed training is a plus
• Experience setting a long-term technical direction and turning it into an achievable roadmap, delivering useful improvements along the way
• A track record of improving ML delivery workflows, and the ability to explain the trade-offs, results, and lessons from those decisions
• 3+ years experience building and operating ML systems in production, including feature pipelines, the training data path, the serving layer, and the monitoring around them
• Fluency with AI tools, including coding agents, in your own engineering work, with the judgment to evaluate their output and own the quality of what you ship

Preferred

• Experience in financial services, credit, fraud, or another regulated decisioning domain, and familiarity with model risk management, is a plus
• Experience leading initiatives from an ambiguous problem through scoping, stakeholder agreement, and delivery