Staff Software Engineer - Credit Insights
Staff Engineer · Staff · Full Time
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Role
What you'll do.
Staff Software Engineer - Credit Insights at Plaid is a senior technical leadership role focused on architecting and scaling cashflow-based credit insights products. You'll design systems supporting online feature serving, offline ML pipelines, and data processing infrastructure while leading engineering teams and partnering with Product, Data Science, and ML teams to power next-generation credit underwriting at scale.
Responsibilities
- Technical Architecture Leadership: Define and evolve the architecture of credit insights products across the entire data lifecycle, from real-time data fetching and low-latency online feature serving for API requests to complex offline production pipelines and machine learning model training infrastructure.
- System Scaling and Optimization: Design and implement scalable solutions to support an anticipated 100x increase in system load while maintaining performance, reliability, and cost efficiency across distributed infrastructure serving millions of users.
- Cross-Functional Product Development: Partner closely with Product Management, Data Science, and Machine Learning teams to develop, validate, and productionize new insights products that enable comprehensive credit underwriting use cases and expand access to lending.
- Technical Mentorship and Team Leadership: Mentor and develop engineering team members, lead architectural decisions, drive technical initiatives, and foster an inclusive, high-performing team culture that attracts and retains top engineering talent.
- Production System Ownership: Build and maintain robust production systems that reliably serve machine learning models in both real-time and batch processing contexts, ensuring system observability, operational excellence, and alignment with business objectives.
Qualifications
What we look for.
Technical
Backend Systems Design and Development
Extensive experience architecting and building scalable backend services and distributed systems. Demonstrated expertise in designing systems that handle high throughput, low latency requirements, and managing complex data flows across multiple services.
Machine Learning Systems Infrastructure
Proven experience building production systems that serve and support machine learning models in both online (real-time inference) and offline (batch processing, model training) contexts. Experience implementing feature stores, model serving infrastructure, or similar ML platforms.
Data Processing and Pipelines
Strong background designing and implementing data processing pipelines, ETL workflows, and handling large-scale data transformations. Experience with distributed data processing frameworks and optimizing performance at scale.
Cross-Functional Technical Communication
Demonstrated ability to communicate complex technical concepts clearly to diverse stakeholders including data scientists, product managers, and non-technical leaders. Experience navigating competing priorities and aligning technical solutions with business goals.
Education
Bachelor's Degree in Computer Science or Equivalent
Formal education in Computer Science, Software Engineering, or related field, or equivalent professional experience demonstrating strong computational thinking and software engineering fundamentals.
Experience
Senior-Level Backend Engineering (8+ Years)
Minimum 8+ years of professional software engineering experience with at least 5+ years focused on backend systems, infrastructure, or platform engineering. Previous experience at scale-focused companies or high-growth environments preferred.
Technical Leadership Track Record
Demonstrated success leading technical projects, making architectural decisions, mentoring junior engineers, and influencing engineering culture. Examples should include leading cross-functional initiatives and owning end-to-end system design.
ML Systems or FinTech Domain Experience (Preferred)
Prior experience in credit, lending, or financial services industries provides valuable context for credit underwriting requirements. Alternatively, experience building infrastructure supporting machine learning or data science workflows is highly valued.
Skills
Required
Distributed Systems Architecture
Expert-level understanding of designing scalable, resilient distributed systems including service design patterns, system decomposition, consistency models, and operational concerns like observability and failure recovery.
Backend Development
Mastery of backend development across one or more programming languages. Ability to design APIs, manage state, handle concurrency, and optimize performance for production workloads serving millions of requests.
ML Model Serving Infrastructure
Strong expertise building systems that integrate machine learning models into production applications. Experience with real-time inference serving, feature engineering, model lifecycle management, and monitoring model performance.
Data Pipeline Architecture
Deep understanding of designing robust, maintainable data pipelines. Experience with batch processing, streaming architectures, data quality frameworks, and ensuring data consistency and lineage.
Technical Leadership
Proven ability to lead technical initiatives, make architectural decisions, mentor team members, and drive engineering best practices. Ability to balance architectural purity with pragmatic business requirements and timelines.
Preferred
Financial Services Domain Knowledge
Nice to havePrior experience in fintech, credit, lending, or banking sectors providing familiarity with regulatory requirements, credit underwriting processes, and financial data handling best practices.
Feature Store or Real-Time Data Platforms
Nice to haveHands-on experience building or operating feature stores, real-time analytics platforms, or similar systems that serve features to multiple downstream consumers with strict latency requirements.
High-Scale Infrastructure Experience
Nice to haveBackground working at high-growth fintech or technology companies where you've addressed scaling challenges, managed massive data volumes, or designed systems handling billions of daily transactions.
Data Science Collaboration
Nice to haveTrack record of successful partnerships with data scientists and ML engineers on production initiatives, demonstrating ability to translate research into robust, maintainable systems.
Open Source Contributions
Nice to haveActive participation in open source projects related to data engineering, backend systems, or machine learning infrastructure, demonstrating commitment to community and modern engineering practices.
Tech stack
Languages
Frameworks
Databases
Tools
Other
Compensation
Pay and benefits.
Base·USD 207,600 – 273,600
Equity·Stock options
Benefits
Comprehensive Medical, Dental, and Vision Coverage
Extensive healthcare benefits including medical, dental, and vision insurance, ensuring employee and family wellness needs are fully supported.
401(k) Retirement Plan
Tax-advantaged retirement savings plan to help you build long-term financial security.
Equity Compensation
Competitive equity packages allowing you to participate in Plaid's long-term success and growth as a company.
Flexible Work Environment
Options for remote work and flexible arrangements, recognizing the importance of work-life balance for high-performing engineers.
Professional Development and Learning
Opportunities for continuous learning, attending industry conferences, and accessing educational resources to stay current with evolving technologies and best practices.
Inclusive and Diverse Culture
Commitment to building a diverse, inclusive team that values different perspectives and lived experiences. Plaid actively supports underrepresented groups in technology.
Accessibility and Accommodations
Dedicated support for candidates and employees with disabilities, including reasonable accommodations throughout the recruiting process and employment.
Process
Interview steps.
- 01
Initial Screening Call
Preliminary conversation with a recruiter to discuss your background, career goals, and alignment with the Staff Engineer role. This 30-minute call focuses on understanding your technical trajectory and interest in Plaid's credit insights mission.
- 02
Technical Architecture Interview
Deep-dive technical conversation with a Staff or Senior Engineer on the Credit Insights team. Expect discussion of system design principles, your past architecture decisions, scaling challenges you've overcome, and how you approach building production ML infrastructure.
- 03
ML Systems and Data Pipeline Assessment
Focused technical interview on experience building systems that integrate machine learning models, designing data pipelines, and handling ML model serving infrastructure. May include discussion of specific technical challenges or take-home design problems.
- 04
Cross-Functional Collaboration Interview
Conversation with Product Management or Data Science stakeholders to assess your ability to partner effectively with non-engineering teams, translate requirements into technical solutions, and drive cross-functional initiatives.
- 05
Leadership and Mentorship Discussion
Interview with a senior leader or engineering manager exploring your approach to technical mentorship, team leadership, architectural decision-making, and how you've influenced engineering culture and best practices.
- 06
Final Executive Interview
Conversation with a Director or VP-level leader to discuss long-term strategic alignment, your vision for credit insights technology, growth potential at Plaid, and organizational fit with leadership team.
Full posting
Original listing.
We believe that the way people interact with their finances will drastically improve in the next few years. We’re dedicated to empowering this transformation by building the tools and experiences that thousands of developers use to create their own products. Plaid powers the tools millions of people rely on to live a healthier financial life. We work with thousands of companies like Venmo, SoFi, several of the Fortune 500, and many of the largest banks to make it easy for people to connect their financial accounts to the apps and services they want to use. Plaid’s network covers 12,000 financial institutions across the US, Canada, UK and Europe. Founded in 2013, the company is headquartered in San Francisco with offices in New York, Washington D.C., London and Amsterdam.
We believe Plaid has the power to be the next-gen Credit Bureau - supporting large scale adoption of cash flow into the credit underwriting process.
The Credit Decisioning platform team is responsible for building best-in-class cashflow based insights products that enable lenders to make more holistic lending decisions and empower broader access to Credit products for prospective borrowers. We own the systems and tooling that form the platform to build and serve these insights at huge scale, partnering with our Data partners to release new products yearly.
You will be defining the future architecture of Credit insights products and executing against an ambitious product roadmap. You will partner with our Product, Data Science, and Machine Learning team to iterate on and productionize new insights that enable our customers to make more holistic lending decisions.
Responsibilities:
Leading technical architecture and execution across credit insights products: everything from data fetching and online feature serving for API requests, to offline production pipelines and tooling for model training.
Scaling and evolving the architecture through an expected ~100x increase in load from deterministic factors
Collaborating closely with Product, Data Science, and Machine Learning partners to develop and scale insights products that enable Credit underwriting use cases
Mentoring engineers and contributing to a strong, inclusive team culture.
Qualifications:
Strong experience building and scaling backend products
Strong technical leadership skills, including mentoring peers, leading projects and driving architectural decisions
Demonstrated success in building and maintaining production systems that serve and support ML models, both in online and offline settings - as well as working closely with data science or ML teams
Experience collaborating with cross-functional stakeholders and with teams and leaders across the engineering function
[nice-to-have] Experience working in the credit or lending space
Our mission at Plaid is to unlock financial freedom for everyone. To support that mission, we seek to build a diverse team of driven individuals who care deeply about making the financial ecosystem more equitable. We recognize that strong qualifications can come from both prior work experiences and lived experiences. We encourage you to apply to a role even if your experience doesn't fully match the job description. We are always looking for team members that will bring something unique to Plaid!
Plaid is proud to be an equal opportunity employer and values diversity at our company. We do not discriminate based on race, color, national origin, ethnicity, religion or religious belief, sex (including pregnancy, childbirth, or related medical conditions), sexual orientation, gender, gender identity, gender expression, transgender status, sexual stereotypes, age, military or veteran status, disability, or other applicable legally protected characteristics. We also consider qualified applicants with criminal histories, consistent with applicable federal, state, and local laws. Plaid is committed to providing reasonable accommodations for candidates with disabilities in our recruiting process. If you need any assistance with your application or interviews due to a disability, please let us know at [email protected].
Please review our Candidate Privacy Notice here.
Additional compensation in the form(s) of equity and/or commission are dependent on the position offered. Plaid provides a comprehensive benefit plan, including medical, dental, vision, and 401(k). Pay is based on factors such as (but not limited to) scope and responsibilities of the position, candidate's work experience and skillset, and location. Pay and benefits are subject to change at any time, consistent with the terms of any applicable compensation or benefit plans.
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