Staff Software Engineer, Current
Machine Learning
+ Stock Options
Online banking & financial services
Posted today
Online banking & financial services
201-500 employees
Posted today
+ Stock Options
201-500 employees
Company mission
To build the future of banking.
Role
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
Our take
Current is a neobank offering digital banking services, with a focus on younger generations and paycheck-to-paycheck customers. It offers a swathe of innovative services including on-demand pay and overdraft protection to people underserved by traditional institutions.
Younger customers are expected in the coming years to pivot from legacy institutions to the higher functionality and lower costs of consumer-first neobanks like Current. By netting an average customer age of 27, Current is establishing itself as a pack leader among rival but less well-funded neobanks like Step or Till.
It is also successfully competing with legacy institutions by exploiting the relatively adaptive nature of neobanking. Current has previously offered customers advanced access to stimulus checks, and is seeking to expand into crypto. The company continues to raise funding which supports its continued growth and an accelerated path to profitability.
Steph
Company Specialist at Welcome to the Jungle
Insights
-2% employee growth in 12 months
Company
Funding (last 2 of 7 rounds)
Jun 2026
$80m
SERIES E
Apr 2021
$220m
SERIES D
Total funding: $472.6m
Company benefits
- Company equity
- Flexible vacation policy
- Commuter benefits
- Discretionary bonus program
- Medical, Dental & Vision
Company HQ
Little Italy, New York, NY
Leadership
Stuart Sopp
(Founder & CEO)Worked as a Trader for BNY Mellon and Deutsche Bank for a combined 9 years before serving as Head of Trading at Citi and Morgan Stanley. Left this role after 5 years to found Current in June 2015, working as CEO ever since.
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