Job description

STAFF ENGINEER, MACHINE LEARNING Current is a leading consumer fintech platform transforming financial access for everyday Americans with over 6 million members. We provide access to financial solutions that seamlessly work together to solve the needs of our members and enable all Americans to build better financial futures. Based in NYC, our results-driven environment drives us to build better products, grow faster and empower everyone on our team to have an impact on our business and mission to improve financial outcomes. Current's Engineering team is dedicated to building our products and infrastructure. With our applications running on Google Cloud Kubernetes Engine, we support a proprietary banking core that can scale to handle millions of transactions a day. Our services run on MongoDB, with data exported to Google Cloud Storage and BigQuery for analytics. Batch feature computation runs on the JVM using Apache Beam and Dataflow, with dbt for the analytics estate and Airflow for orchestration. Our ML stack includes an in-house feature store and model registry with a serving proxy, and our data scientists develop models in Python. Machine learning drives decisions across the business: underwriting for liquidity products, fraud detection and risk exposure, marketing acquisition and spend. Models that ship faster and behave predictably in production are worth real money and real member trust. We are looking for a Staff Engineer, Machine Learning to join our Infrastructure team in New York. This role has a salary range of $240,000 - $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

Requirements

Must-haves

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
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
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
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

Nice-to-haves

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

Application prep

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Keywords to mirror

machine learningkubernetesmlpythonsqlai

Platform: greenhouse
Effort: quick

Benefits

Benefits detected in the listing.

Dental

Vision

401(K)

Parental Leave