About this role
Engineers who thrive on turning ambiguous problems into reliable production systems will find a strong fit in this position. Current is hiring a Staff Software Engineer to lead machine learning infrastructure initiatives from their New York office, focusing on streamlining the end-to-end model lifecycle for a fintech platform serving over six million members.
The engineering team supports a proprietary banking core using Google Cloud Kubernetes Engine, MongoDB, BigQuery, Apache Beam, and Airflow. In this hands-on individual contributor role, the incoming engineer will own the technical direction of the machine learning stack, build tooling for training and serving consistency, and treat data scientists as their primary customers to help models ship faster and behave predictably in production.
What you will do
- Own the technical direction for the machine learning stack end to end, covering feature definition, training infrastructure, model serving, and production monitoring.
- Build tooling that ensures training and serving consistency across analytics, batch computation, and live serving environments.
- Design systems that link every deployed model to its dataset, feature versions, labels, and training code according to model risk management standards.
- Enable data scientists to generate reproducible, point-in-time-correct datasets and execute standard validations independently.
- Establish working contracts between the groups that build, consume, and govern models while keeping the stack legible across teams.
- Measure delivery time, engineering effort, and rework to prioritize infrastructure improvements and prove workflows on production models.
What you need
- Bring at least 8 years of overall software engineering experience, including strong production skills in Python and SQL.
- Possess a minimum of 3 years of experience building and operating machine learning systems in production, covering feature pipelines, training data paths, serving layers, and monitoring.
- Demonstrate at least 3 years of experience building and maintaining machine learning platforms alongside production development in a JVM language.
- Show a track record of improving machine learning delivery workflows and explaining the trade-offs and results behind those decisions.
- Apply sound reasoning regarding time in data, such as point-in-time correctness, label leakage, feature availability, and training-serving skew.
- Exhibit experience setting long-term technical direction, turning it into achievable roadmaps, and leading initiatives from ambiguity to delivery.
- Maintain fluency with AI tools in your own engineering work while evaluating output with strong personal accountability for quality.
Nice to have
- Feature store, feature platform, or ML platform experience at a company where models make consequential decisions
- Experience in financial services, credit, fraud, or another regulated decisioning domain
- Familiarity with model risk management
- Experience with streaming and change data capture, large-scale batch on Apache Beam or Spark, or distributed training
Benefits
- Competitive salary and meaningful equity in the form of stock options
- 401(k) plan and discretionary performance bonus program
- Medical, Dental and Vision premiums covered at 100% for employees and dependents
- Flexible time off, paid holidays, and a generous parental leave policy
- Commuter benefits, fitness benefits, and healthcare/dependent care FSA benefits
- Mental health employee assistance programs, healthcare advocacy, and catered lunches at the New York office
Skills and tech stack
Questions candidates ask
Where is this role located?
This role is based in New York, NY, working out of the company's modern office with an open floor plan, stocked kitchen, and catered lunches.
What is the salary range for this position?
The salary range for this role is $265,000 to $325,000, complemented by meaningful equity in the form of stock options and a discretionary performance bonus program.
Is this a management role with direct reports?
This is a hands-on individual contributor role without direct reports, though it offers room to grow into a team.
What does the initial focus look like in the first year?
In the first year, you will establish a delivery baseline, prove the workflow on one production model with versioned features and reproducible datasets, and extend those capabilities to additional models.