STAFF ENGINEER, MACHINE LEARNINGWe are looking for a Staff Engineer, Machine Learning to join our Infrastructure team in New York. This role has a salary range of $265,000 - $325,000. You will lead ML engineering initiatives across Current, with the goal of optimizing our model lifecycle: improving how we build, validate, deploy, and change models, and making that path faster and more repeatable as our model portfolio grows. This is a hands-on individual contributor role without direct reports, with room to grow into a team. The ideal candidate has built and operated ML systems in production end to end, not only models, and has a track record of setting technical direction and delivering against it. This person should be comfortable leading from an ambiguous problem to a shipped solution, and should treat data scientists as their customer.
WHAT TO EXPECT:- 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
ABOUT YOU:- 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
BENEFITS:- Competitive salary
- Meaningful equity in the form of stock options
- 401(k) plan
- Discretionary performance bonus program
- Biannual performance reviews
- Medical, Dental and Vision premiums covered at 100% for you and your dependents
- Flexible time off and paid holidays
- Generous parental leave policy
- Commuter benefits
- Fitness benefits
- Healthcare and Dependent care FSA benefit
- Employee Assistance Programs focused on mental health
- Healthcare advocacy program for all employees
- Access to mental health apps
- Team building activities
- Our modern NYC based office with open floor plan, stocked kitchen, and catered lunches
