Staff Software Engineer

Who you are

• 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
• A track record of improving ML delivery workflows, and the ability to explain the trade-offs, results, and lessons from those decisions
• 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
• Sound reasoning about time in data: point-in-time correctness, label leakage, feature availability, and training/serving skew
• Experience setting a long-term technical direction and turning it into an achievable roadmap, delivering useful improvements along the way
• Experience leading initiatives from an ambiguous problem through scoping, stakeholder agreement, and delivery
• 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
• 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
• Feature store, feature platform, or ML platform experience at a company where models make consequential decisions is a plus
• Experience in financial services, credit, fraud, or another regulated decisioning domain, and familiarity with model risk management, is a plus
• Experience with streaming and change data capture, large-scale batch on Apache Beam or Spark, or distributed training is a plus

What the job involves

• Owning technical direction for the ML stack end to end: feature definition and computation, training data generation, training infrastructure, model serving, and production monitoring, along with the contracts between them
• Building tooling for training/serving consistency across analytics, batch computation, and live serving, accounting for differences in data sources and timing
• Designing how every deployed model stays linked to its dataset, feature versions, labels, and training code, to the standard model risk management expects
• Enabling data scientists to generate reproducible, point-in-time-correct datasets and run standard validation without an engineering ticket
• Setting the working contracts between the groups that build, consume, and govern models, and keeping the stack legible to people who don't read the code
• Measuring delivery time, engineering effort, and rework, and using that evidence to prioritize improvements
• In your first year:
• Establishing a delivery baseline and proving the workflow on one production model with versioned features, a reproducible dataset, and reusable validation
• Extending those capabilities to additional models and measuring adoption and improvement against the baseline
• Standardizing model monitoring and defining production-readiness gates with Data Science, Risk, and service owners
• Evaluating build-versus-buy options for ML platform tooling against real production requirements
• Partnering daily with engineers across our squads and with data scientists and analysts, and regularly with Risk, Marketing, and Finance, who own the decisions our models support

Benefits

• Company equity
• Flexible vacation policy
• Commuter benefits
• Discretionary bonus program
• Medical, Dental & Vision