Engineering Manager- Data and AI

Banking is being reimagined—and customers expect every interaction to be easy, personal, and instant. 

We are building a universal banking assistant that millions of U.S. consumers can use to transact across all financial institutions and, over time, autonomously drive their financial goals. Powered by our proprietary BankGPT platform, this assistant is positioned to displace age-old legacy systems within financial institutions and own the end-to-end CX stack, unlocking a $200B opportunity and potentially replacing multiple publicly traded companies. 

Ultimately, our mission is to drive financial well-being for millions of consumers.

With over two-thirds of Americans living paycheck to paycheck, 50% holding less than $500 in savings, and only 17% financially literate, we aim to put financial well-being on autopilot to help solve this problem.

About the Role

We are hiring a deeply technical, hands-on Engineering Manager to lead our Data Engineering, Data Platform, and Applied Machine Learning / Data Science efforts.

This is a builder-first leadership role. You will design, build, and operate critical data and ML systems while leading a team of senior engineers and data scientists and work with the stakeholders for setting technical direction. You will own the data and ML foundations that power analytics, experimentation, AI-driven products, and autonomous systems across the company.

What You’ll Do

Hands-On Coding, Architecture & Engineering

• Design and build data pipelines supporting high-volume, low-latency workloads.
• Write and review production-quality code in Python / Go, guiding schema design, partitioning, and performance tuning.
• Design and implement data pipelines, feature stores, and real-time inference systems.
  Architect end-to-end data and ML systems across ingestion, transformation, storage, feature generation, and serving layers.
• Debug production issues across data correctness, model performance, latency, and system scalability.
• Make architectural trade-offs between lake house, warehouse, streaming, and real-time inference systems.
  
  

Data and LLM Platform Ownership

• Own and evolve the core data platform supporting analytics, experimentation, and ML workloads.
• Build and operate modern data systems using distributed compute, streaming platforms, and cloud-native storage.
• Design feature pipelines and data services consumed by ML models and product teams.
• Implement semantic layers and data APIs to ensure metric consistency and reuse.
• Partner with infrastructure teams on reliability, capacity planning, and cost optimization.
• Architect end-to-end RAG systems (embedding pipelines, vector DBs, retrieval layers).
• Design evaluation systems for LLM outputs (automated scoring, guardrails).

Machine Learning & Data Science Leadership

• Lead teams building applied ML models, analytics, and experimentation frameworks.
• Collaborate with data scientists to productionize models, from offline training to online inference.
• Own ML data workflows including feature engineering, model evaluation, monitoring, and retraining pipelines.
• Enable experimentation platforms, A/B testing, and feedback loops for continuous learning.
• Drive best practices around model performance, bias detection, and explainability.
  
  

Quality, Governance & Observability

• Establish data and ML quality standards, validation, and anomaly detection.
• Implement observability across pipelines and models (metrics, alerts, drift detection).
• Enforce data governance, PII handling, access controls, and auditability.
• Define SLAs/SLOs for data freshness, model reliability, and system availability.
• Partner closely with Security and Compliance teams to meet regulatory requirements.
  
  

Technical Leadership & People Management

• Lead and mentor data engineers, ML engineers, and data scientists.
• Set technical standards for architecture, code quality, testing, and documentation.
• Drive sprint planning, execution, and delivery accountability.
• Hire, onboard, and grow senior engineers and scientists capable of owning complex systems.
• Foster a culture of ownership, rigor, and continuous technical improvement.
  
  

Cross-Functional Collaboration

• • Work closely with Product, AI, Platform, Security, and Compliance teams
  • Translate business and product requirements into scalable data and ML systems.
  • Communicate architectural decisions, risks, and trade-offs clearly to leadership.

Required Qualifications

• 8+ years of experience building data-intensive and ML-driven systems.
• 2+ years of experience managing engineers and/or data scientists while remaining hands-on.
• Demonstrated startup experience (Series A–D preferred).
• Strong expertise in programming languages like Node.js, Python or Golang; experience with distributed data processing frameworks.
• Hands-on experience with streaming systems and real-time data processing.
• Experience designing and operating data lakes, warehouses, or lake house architectures.
• Experience supporting ML training, feature pipelines, and online inference in production.
• Deep understanding of data modeling, performance optimization, and system reliability.
• Strong debugging and operational experience in cloud environments.
  

Preferred Qualifications

• Experience enabling AI-first or ML-heavy products.
• Familiarity with experimentation platforms, model evaluation, and monitoring.
• Experience in regulated or enterprise-scale environments.
• Experience in high-growth startup environments.
• Prior background as a Staff or Principal Engineer before moving into management.

What Makes This Role Special?

• You’ll shape the core AI that powers agentic intelligence for financial systems serving millions of users.
• You’ll own a research-meets-engineering mandate — from exploring new models to bringing them to life in production.
• You’ll define how autonomous AI systems learn, adapt, and remain safe in a regulated environment.
• You’ll work with a team combining AI research, applied data science, and product engineering, moving fast with purpose and rigor.

We value in-person time for planning, problem-solving, and connection. Team members in this role must live within commuting distance of our Palo Alto or San Francisco hub.

What We Offer

• 💡 100% paid health, dental & vision care
• 💰 401(k) match & financial wellness perks
• 🌴 Discretionary PTO + paid parental leave
• 🧠 Mental health, wellness & family benefits
• 🚀 A mission-driven team shaping the future of banking

At interface.ai, we are committed to providing an inclusive and welcoming environment for all employees and applicants. We celebrate diversity and believe it is critical to our success as a company. We do not  discriminate on the basis of race, color, religion, national origin, age, sex, gender identity, gender expression, sexual orientation, marital status, veteran status, disability status, or any other legally protected status. All employment decisions at Interface.ai are based on business needs, job requirements, and individual qualifications. We strive to create a culture that values and respects each person's unique perspective and contributions. We encourage all qualified individuals to apply for employment opportunities with Interface.ai and are committed to ensuring that our hiring process is inclusive and accessible.