The Team + The Role

Pendo’s Customer Engineering team is the technical backbone of the pre- and post-sales customer motion. The team brings together work that has historically sat across Customer Success, Technical Account Management, and Solutions Engineering into one full-lifecycle technical owner. Customer Engineering helps customers connect technical execution to business outcomes and realize the value of their investment in Pendo.

As a Senior Customer Engineer, you own strategic and complex Enterprise accounts across acquisition, implementation, adoption, expansion, and escalation. You lead technical strategy for complex deployments, resolve ambiguous customer challenges, and partner closely with account teams to drive retention, expansion, and customer value. Your impact extends beyond your own accounts through playbooks, coaching, repeatable fixes, and workflow improvements that raise the capability of the broader Customer Engineering team.

This is a remote role supporting a South East territory. 

What this looks like day-to-day

• New customer acquisition and selling: Partner with Account Directors on pre-sales motions for new customer acquisition, product expansion, and expansion across lines of business. You identify customer pain, craft and deliver tailored demos, and lead technical evaluations through requirements gathering, success criteria, installation guidance, and hands-on workshops.
• Complex implementations and value realization: Lead onboarding for strategic customers, including planning, implementation, integrations, and training. You build customer-specific implementation playbooks, guide complex deployments with executive visibility, and ensure customers realize value during onboarding and over time.
• Adoption, expansion, and growth: Identify opportunities to expand adoption across underutilized features, untapped products, and new lines of business. When adoption stalls, you diagnose root causes, design a path forward, and connect technical work to measurable business outcomes.
• Technical escalation: Serve as an escalation point for complex technical challenges raised by other Customer Engineers. You resolve ambiguous problems with multiple variables and tradeoffs, involving Product or Engineering only when truly needed.
• Team capability building: Build playbooks, document repeatable fixes, coach peers, and improve how Customer Engineering work gets done across the team. You use AI tools to accelerate content creation, surface patterns across accounts, and share practical workflow findings with the broader team.
• Early account impact: Assess account health, risks, stakeholder relationships, and the highest-priority problems to solve within your portfolio. You establish trust with Account Directors and customer stakeholders, then independently lead complex customer interactions that move accounts forward.
• Quarterly business outcomes: Drive measurable improvement in customer health, retention, expansion, and technical resolution across strategic accounts. You contribute reusable frameworks, playbooks, or processes that other Customer Engineers use and become a trusted advisor to Account Directors on account health and technical strategy.

Who You Are

Beyond the qualifications, we hire through a specific lens. These aren't buzzwords; they're the things we'll actually look for in how you talk about your work.

You're a builder, not a maintainer.

You're most energized when there isn't a clear path yet, and you get to define it. You don't wait for direction; you identify gaps, shape solutions, and drive them forward. At Pendo, great Senior Customer Engineers don't just follow instructions; they operate as strategic advisors, influencing decisions, guiding stakeholders, and elevating how we work.

You're AI-curious - genuinely.

You're not using AI tools occasionally. You're rewiring how you work around them. You're faster, sharper, and more prolific because of it, and you bring that energy to everything — how you approach your work, how you prep, how you communicate, how you think. We want someone who sees AI as a multiplier, not a shortcut.

Must-haves

• 5 to 8 years of experience in a customer-facing technical role such as Sales Engineering, Solutions Engineering, Technical Account Management, Customer Engineering, implementation, professional services, or solutions architecture.
• Deep hands-on experience with web technologies including HTML, CSS, JavaScript, and REST APIs, with the ability to architect and troubleshoot complex front-end implementations.
• Proven experience managing strategic Enterprise accounts with executive-level stakeholders, competing priorities, and complex technical or business requirements.
• Demonstrated ability to solve ambiguous problems involving multiple variables, constraints, and tradeoffs.
• Experience connecting technical execution to business outcomes such as retention, expansion, ROI, and value realization.
• Hands-on use of AI tools for account research, content creation, or workflow automation, with demonstrated integration into day-to-day work rather than light experimentation.
• Experience mentoring or coaching more junior technical team members.

Nice-to-haves

• Experience with Pendo, digital adoption platforms, product analytics, or in-app experience tooling in an enterprise environment.
• Hands-on experience with mobile app development, including Swift, Android, or React Native.
• Familiarity with data architecture, AI or machine learning concepts, or advanced analytics instrumentation.
• Track record of building reusable frameworks, playbooks, or processes that were adopted by a broader team.

About Pendo

Pendo was founded in 2013 by former product managers, who combined their heads and hearts to build something they wanted but never had as product managers: a simple way to understand and attack what truly drives product success. Our mission is to improve society's experience with software. Come join one of the fastest-growing startups, supported by best-in-class institutions like Battery Ventures, Salesforce Ventures, Spark Capital and Meritech.

Pendo Core Values: Bias to Act, Hone Your Craft, The Team is Pendo, and Maniacal Focus.

Location: Remote

Compensation: The expected On-Target Earnings (OTE) range for this role is $193,393 - $241,742, with a 70/30 base-to-variable compensation split.

Benefits: Pendo offers highly competitive United States benefits, including employer-heavy coverage with $0 premium options, strong 401(k) match, equity, and flexible time off.

EEOC: We are an equal opportunity employer and believe having diverse teams where everyone brings their whole self to Pendo is key to our success. We welcome all people of different backgrounds, experiences, abilities and perspectives.

Accessibility: Pendo is committed to working with, and providing access and reasonable accommodation to, applicants with mental and/or physical disabilities. If you think you may require an accommodation for any part of the recruitment process, please send a request to: accommodation@pendo.io. All requests for accommodations are treated discreetly and confidentially, as practical and permitted by law.

About the Role

At Opendoor, AI isn't a side project - it's how we work. Across the company, teams default to AI to solve problems, ship faster, and remove friction from everything we do. The AI Operations team sits at the intersection of data, AI engineering, and operational systems. 

You will help define the future of how we work across our Operations by building AI powered workflows, automation, and experiences that drive leverage across Operations. This is high agency work in high ambiguity - turning messy problems into clean solutions, prototyping fast, and shipping.

You'll design systems and influence tooling decisions that affect how our operations scale – so we need people who think critically about what they build, why, the impact it will have on our operations and how to measure success. 

We're looking for AI-native builders to join our team, blending Operational expertise and systems thinking to drive leverage across our teams. If you default to AI in how you approach work, you’ll fit right in. 

Why Join Us

• Opportunity to shape AI Operations from the ground up
• Work on cutting-edge systems directly impacting our Customers and Operations 
• Collaborate with talented engineers, data scientists, product teams, and operational teams 

What You'll Do

• Turn ambiguous business problems into clear system designs, experiments, and implementation plans
• Identify and implement opportunities to improve our operational reliability, efficiency, and cost
• Prototype and ship rapid experiments that test improvements to workflows
• Evaluate build-vs-integrate decisions for AI tools and automation, with a bias toward scalable, maintainable solutions over one-off demos
• Build and maintain AI-powered automation, internal tools to improve how we work
• Assess and pressure-test system architectures — your own and others' — for scaling limitations, failure modes, and operational sustainability
• Maintain clean, trustworthy data through thoughtful schemas, audits, and dashboards that improve visibility and decision making
• Implement monitoring and alerting for model performance, latency, and drift detection
• Troubleshoot production issues and conduct root cause analysis

• Partner with data scientists and R&D to operationalize models
• Develop runbooks, documentation, and best practices for AI Operations

What We're Looking For

AI Alchemy: You have high agency and a tinkering mindset. You default to experimenting, running variations with new tools and methods - LLMs, automation platforms, coding assistants until you can transmute data into high quality results. You're comfortable writing SQL, working with APIs, and stitching tools together to ship solutions fast.

Systems Thinking: You understand how data flows, business and systems objectives, how decisions get made, and how changes in one part of an operation ripple across others. You evaluate global vs. local trade-offs and can explain why a solution that works today might break at scale - before someone has to ask.

System Design: You take complex, messy problems and reduce them to simple, durable architecture. You know when to build custom vs. integrate existing tools, and you proactively identify scaling limitations before they become technical debt.

Communication: Articulate problems clearly to both technical and non-technical audiences. You write clean documentation, build dashboards that tell a story, and can walk a stakeholder through your design rationale.

Impact Driven: You optimize for impact over novelty, choosing the right tools for the job rather than chasing trends with a long range horizon, observability to ensure performance, scalability and resiliency 

Additionally:

• Familiarity with LLM deployment, context engineering and comfortable writing SQL 
• Understanding of machine learning lifecycle concepts: training, evaluation, deployment, monitoring
• 5+ years of experience in operations and data experience 

Tools and Technologies

You'll work across a range of tools and we expect this list to evolve. Current stack includes:

• AI coding assistants: Claude Code, Cursor, or similar 
• Rapid prototyping tools: Lovable, Replit, Figma
• Workflow automation: Gumloop, Zapier, or similar platforms
• Experience with MCP integrations is a plus
• Experience with data platforms (Snowflake, Databricks) is a plus
• Background in agentic AI frameworks or building automation flows is a plus

Bonus Domain Expertise

You don't need deep expertise in all of these, but experience in one or more is a strong signal

• Pricing and valuation
• Sales,support, partnerships 
• Home operations 
• Homes support 

ABOUT THE ROLE 

This role sits within Anaplan's IT Customer Experience organization and is focused on transforming how Anaplan employees work. You will design, build, and scale AI tools, agents, and capabilities that drive productivity, smarter decision-making, and operational efficiency across our global workforce. 

Anaplan is building its Enterprise AI Center of Excellence (COE) — a one-stop shop for all internal AI at Anaplan — and is looking for an AI Principal Architect to lead it from a technical standpoint. This is a senior, newly created role at the intersection of hands-on enterprise AI engineering, internal system architecture, technical leadership, and workforce enablement. 

The AI Principal Architect will own the technical vision for Anaplan's internal AI — designing the reference architecture for LLMs, agentic AI, and Gemini Enterprise; setting engineering standards; evaluating emerging platforms; and ensuring every employee-facing initiative is built with rigor, scalability, and responsible design. 

This role carries a hybrid profile: deep individual technical contribution combined with team leadership influence. You will mentor engineers, guide architectural decisions, serve as the primary technical escalation point, and contribute a forward-looking perspective to the 2–3 year AI technology roadmap — without necessarily holding formal management authority over all contributors. 

You will report directly to the Director, IT Customer Experience, who co-leads the Enterprise AI COE alongside the Financial Flexibility Lead and a GTM Champion as a 3-in-a-box. You will collaborate closely with Technical/Engineering teams (dotted-line, for agent development and delivery) and the Transformation & Financial Flexibility team (to identify AI-driven cost-saving opportunities). 

KEY RESPONSIBILITIES 

Reference Architecture & Enterprise AI Engineering 

• Own and publish the reference architecture for Anaplan's internal AI — covering LLMs, agentic AI frameworks, Gemini Enterprise integrations, and data pipelines. 

• Architect end-to-end internal enterprise AI solutions designed for the scale, security, and reliability requirements of a global workforce. 

• Set and enforce AI engineering standards, coding practices, code review processes, and technical guardrails across all internal AI projects. 

• Lead hands-on technical design reviews and guide implementation decisions from intake through production deployment. 

• Evaluate, select, and onboard new enterprise AI platforms and tools — providing technical recommendations grounded in internal workforce needs. 

• Design the LLM abstraction layer and multi-model strategy to optimize cost, performance, and flexibility. 

• Own technical debt management, system observability, and scalability planning for Anaplan's internal AI systems. 

AI Platform Governance & Enablement 

• Define and maintain technical evaluation criteria for the AI intake process — assessing feasibility, build complexity, security risk, and integration requirements for employee-facing tools. 

• Ensure all internal AI initiatives pass engineering, security, compliance, and responsible AI review before development begins. 

• Maintain enterprise AI technical policies, engineering standards, and responsible AI guardrails in partnership with Security and Compliance. 

• Translate approved internal use cases into clear technical specifications and hand off to engineering teams with full architectural context. 

• Define enterprise AI data layer and integration architecture across core systems (Salesforce, ServiceNow, Gainsight, Workday). 

• Integrate with ServiceNow AI Control Tower to track all AI use cases, agents, and realized business value. 

• Implement human-in-the-loop, explainability, and auditability frameworks for all decision-impacting AI systems. 

• Partner with Finance to define and measure AI-driven ROI, ensuring alignment with enterprise value targets. 

• Establish full lifecycle management for AI agents, including monitoring, feedback loops, and continuous improvement. 

Technical Team Leadership 

• Provide technical direction, coaching, and mentorship to engineers across the AI Governance & Enablement and AI Operations & Insights teams. 

• Serve as the primary technical escalation point for complex internal AI delivery challenges and architecture decisions. 

• Partner with Technical/Engineering teams (dotted-line) on agent architecture, build quality, and delivery timelines. 

• Help define the technical bar for AI team hiring and contribute to onboarding and skills development programs. 

• Foster a culture of engineering rigor, experimentation, and continuous improvement across the internal AI function. 

AI Operations & Workforce Adoption 

• Oversee the technical deployment and operationalization of AI agents and solutions, ensuring production readiness and stability for Anaplan employees. 

• Drive Gemini Enterprise adoption and lead AI Friday sessions with genuine technical depth. 

• Build and maintain engineering dashboards tracking internal AI system performance, adoption rates, and ROI metrics. 

• Translate engineering decisions into executive-ready business value narratives for the Director, CIO, and Finance audiences. 

Innovation, R&D & Emerging Technology 

• Research and prototype emerging AI/ML techniques, large language models, agent frameworks, and enterprise tooling to identify internal productivity and cost-saving opportunities. 

• Design and lead proof-of-concept pilots that validate novel internal use cases before committing to full engineering investment. 

• Build and maintain the AI innovation pipeline — a living backlog of high-potential experiments. 

• Contribute the technical perspective on where internal AI capabilities should be invested over the next 2–3 years, collaborating with the Director on the roadmap. 

Cross-Functional Technical Partnership 

• Work hand in hand with the Transformation & Financial Flexibility team to identify and technically validate AI-driven cost-saving opportunities in Anaplan's internal operations. 

• Act as technical advisor and AI subject matter expert to business units identifying internal AI use cases. 

• Support the 3-in-a-box COE co-leadership (IT, Finance, GTM) with technical analysis, architecture reviews, and feasibility assessments. 

• Build trusted technical relationships across Engineering, Security, Compliance, Legal, and HR. 

REQUIRED QUALIFICATIONS 

• 7+ years of software or AI/ML engineering experience, with at least 3 years in a technical lead, staff engineer, or principal-level role — ideally with exposure to enterprise IT or internal tooling environments. 

• Deep hands-on expertise across the AI/ML stack: LLMs, retrieval-augmented generation (RAG), agent frameworks, or MLOps pipelines — with experience in enterprise or workforce deployment contexts. 

• Proven experience architecting and deploying enterprise-scale internal AI systems from design through production, including reliability, observability, and performance engineering. 

• Proficiency in Python and enterprise cloud AI platforms (GCP Vertex AI, Azure AI, or AWS SageMaker) and AI orchestration tooling. 

• Hands-on experience with Google Workspace AI and Gemini Enterprise, or equivalent enterprise AI platforms (e.g., Microsoft Copilot, OpenAI Enterprise). 

• Demonstrated ability to provide technical leadership, mentorship, and architectural guidance to engineering teams without formal management authority. 

• Ability to manage competing priorities, dependencies, and delivery timelines across multiple workstreams. 

• Strong business acumen and communication skills — able to translate complex enterprise AI/ML concepts into business value narratives for C-level audiences. 

• Experience in enterprise SaaS environments with complex stakeholder landscapes, security requirements, and compliance obligations. 

PREFERRED QUALIFICATIONS 

• Experience with AI governance frameworks, responsible AI principles, or enterprise AI policy and risk management for internal deployments. 

• Familiarity with ServiceNow or similar ITSM platforms and integration patterns. 

• Background in enterprise change management, internal AI enablement, or large-scale digital transformation programs. 

• Experience working with cross-functional finance or business transformation teams on AI-driven cost optimization. 

• Prior experience in an internal IT or enterprise technology function at a high-growth SaaS company. 
• Enterprise technology function at a high-growth SaaS company. 

ABOUT ROCKET MONEY 🔮

Rocket Money’s mission is to meaningfully improve the financial prosperity of millions of people. Rocket Money offers members a unique understanding of their finances and a suite of valuable services that save them time and money – ultimately giving them a leg up on their financial journey.

ABOUT THE TEAM 🤝

Data Platform Engineers at Rocket Money further our mission by building and maintaining the infrastructure that enables our company to understand our users and products through reliable, scalable data systems. We build the foundational platform that ingests, processes, and serves data—enabling our Data Analytics, Machine Learning, and Software Engineering teams to build data products efficiently. We work closely with engineering teams to ensure data is captured correctly at the source, design resilient pipelines, and create self-service capabilities that empower stakeholders.

The Data Platform team is in an exciting phase of growth and formalization. While we have established tools and processes in place (dbt, BigQuery, Terraform), we're actively building the comprehensive standards, practices, and systems that will scale with Rocket Money as it grows. We're looking for engineers who can take ownership and evolve workflows into reliable, well-documented, production-grade systems. This is an opportunity to shape architectural decisions, establish team practices, and define how Rocket Money works with data for years to come.

We have a strong preference for process-oriented systems thinkers who excel in balancing stakeholder requirements, technical debt, and organizational complexity—and who thrive in environments where they need to create structure from ambiguity. You'll need to be comfortable making principled decisions, taking ownership not just of code but of outcomes, and operating with significant autonomy as we build the platform together.

ABOUT THE ROLE 🤹

In this role, you will:

• Be an end-to-end owner of our data platform infrastructure, ensuring its security, usability, and performance. Work closely with analytics engineers, machine learning engineers, and software engineers to ensure the platform meets their needs.
• Make collaborative decisions about data tooling, pipeline design, and governance, and implement opinionated interfaces that facilitate easy and best-practice-aligned development for other teammates.
• Continuously improve failure rates for data sources by moving alerting “to the left”; i.e. catching and quarantining bugs/failures as close to the source as possible.
• Analyze patterns in how source data is generated, modeled, and consumed. Work with stakeholders to implement the best solution when new data sources are added.
• Take ownership of existing tools and workflows that may be functional but lack formal structure, documentation, or reliability measures. You'll assess what's working, identify gaps, and systematically improve systems to production-grade quality.
• Document everything you build knowing that you're creating the foundation others will build upon. Your runbooks, architecture decision records, and system documentation will become the institutional knowledge of our data platform.
• Proactively communicate with multiple stakeholders on platform capabilities, technical constraints, architectural decisions, project priorities, and platform support.
• Confidently juggle multiple projects and priorities in our fast-paced environment and work with stakeholders and platform teammates to ensure infrastructure changes, migrations, and improvements are delivered on schedule.
• Automate aggressively and deliberately; anything from GitHub Actions to Slack Workflows to minimize repetitive tasks. Effectively judge when the level of effort is appropriate and avoid over-engineering.

ABOUT YOU 🦄

• You have 6+ years of experience working with data infrastructure, data engineering, or platform engineering within a fast-paced environment. You are highly proficient with SQL, Python, and cloud-based Infrastructure-as-Code (e.g. Terraform), and comfortable working with bash/shell scripting.
• You have 4+ years of production experience with modern data stacks including data warehouses (BigQuery, Snowflake, or Redshift), orchestration tools, managed ingestion services, and infrastructure as code (Terraform, Pulumi, or CloudFormation).
• You have 2+ years of experience building and maintaining production data pipelines, whether through ELT tools, custom applications, streaming systems, or event-driven architectures.
• You've successfully "professionalized" data infrastructure before—taking scrappy, working systems and evolving them into reliable, well-documented, production-grade platforms. You can articulate what "production-ready" means in a data context.
• You have a bias toward action and aren't paralyzed by imperfect solutions. You understand when "good enough for now with a plan to improve" beats "perfect but six months late." You ship incrementally and iterate based on feedback.
• You're comfortable being the first person to tackle a problem. You don't need extensive mentorship or detailed tickets—you can take a high-level business need and figure out the technical approach. That said, you know when to ask for help and can articulate what you need.
• You take ownership seriously—not just of writing code, but of outcomes. When you build something, you implement monitoring, write runbooks, create alerts, and ensure it can be maintained by others. You think about the full lifecycle of systems, not just initial delivery.
• You have strong opinions, weakly held. You can make and defend architectural decisions, but you're open to feedback and willing to change course when presented with better information. You can disagree and commit.
• You understand that "building from scratch" doesn't mean rejecting existing tools—it means thoughtfully selecting, configuring, and integrating managed services and open-source solutions to create a cohesive platform. You know when to build and when to buy.
• You have experience making big changes to critical data infrastructure. You’ve successfully re-architected, migrated, or upgraded data tooling that has strict SLA’s, without significantly affecting downstream stakeholders.

Bonus points if:

• You have led a data infrastructure migration or modernization project where you defined the vision, approach, and implementation.
• You have created internal tools, frameworks, or CLIs that improved how teams work with data (not just one-off scripts).
• You have established data platform best practices like CI/CD workflows, testing frameworks, or observability standards where none existed.
• Expertise in cloud platforms and technologies analogous to our stack:
  • Our stack: GCP (BigQuery, Datastream, Cloud Functions, Vertex AI, GCS), dbt, Fivetran, Postgres, Python, Terraform, Looker, Retool
  • Analogous experience: AWS (Redshift, DMS, Lambda, SageMaker, S3) or Azure (Synapse, Data Factory, Functions), Snowflake, Airbyte/Stitch, infrastructure as code tools, BI platforms

WE OFFER 💫

• Health, Dental & Vision Plans
• Life Insurance
• Long/Short Term Disability
• Competitive Pay
• 401k Matching
• Team Member Stock Purchasing Program (TMSPP)
• Learning & Development Opportunities
• Tuition Reimbursement
• Unlimited PTO
• Daily Lunch, Snacks & Coffee (in-office only)
• Commuter benefits (in-office only)

Additional information: Salary range of $160,000 - $200,000 + bonus + benefits. Base pay offered may vary depending on job-related knowledge, skills, and experience.

Rocket Money, Inc. is an Affirmative Action and Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, or protected veteran status and will not be discriminated against on the basis of disability.

Pursuant to the San Francisco Fair Chance Ordinance, we will consider for employment qualified applicants with arrest and conviction records.

We are seeking a Data Engineer to join our dynamic team. The ideal candidate is an enthusiastic problem-solver who excels at building scalable data systems and has hands-on experience with Databricks, Looker, AWS, MongoDB, PostgreSQL, and Terraform. You will work alongside sales, customer success and engineering to design, implement, and maintain the operational data infrastructure that powers our analytics and platform offerings.

What you'll do:

1. Data Pipeline & Integration
• Design, build, and maintain end-to-end data pipelines using Databricks (SparkSQL, PySpark) for data ingestion, transformation, and processing.
• Integrate data from various structured and unstructured sources, including medical imaging systems, EMRs, Change-Data-Capture from SQL Databases, and external APIs.
2. Analytics & Visualization
• Collaborate with the analytics team to create, optimize, and maintain dashboards in Looker.
• Implement best practices in data modeling and visualization for operational efficiency.
3. Cloud Infrastructure
• Deploy and manage cloud-based solutions on AWS (e.g., S3, EMR, Lambda, EC2) to ensure scalability, availability, and cost-efficiency using IaC tooling (Terraform and Databricks Asset Bundles).
• Develop and maintain CI/CD pipelines for data-related services and applications.
4. Database Management
• Oversee MongoDB and PostgreSQL databases, including schema design, indexing, and performance tuning.
• Ensure data integrity, availability, and optimized querying for both transactional and analytical workloads.
5. Security & Compliance
• Adhere to healthcare compliance requirements (e.g., HIPAA) and best practices for data privacy and security.
• Implement error handling, logging, and monitoring frameworks to ensure reliability and transparency.
• Implement data governance frameworks to maintain data integrity and confidentiality.
6. Collaboration & Documentation
• Work cross-functionally with data scientists, product managers, and other engineering teams to gather requirements and define data workflows.
• Document data pipelines, system architecture, and processes for internal and external stakeholders.

About you:

• Education & Experience
• 7+ years of professional experience in data engineering or a similar role.
• Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field.
• Technical Skills
• Databricks (Spark): Proven expertise in building large-scale data pipelines.
• Looker: Experience in creating dashboards, data models, and self-service analytics solutions.
• AWS: Proficient with core services like S3, EMR, Lambda, IAM, EC2, etc.
• MongoDB & PostgreSQL: Demonstrated ability to design schemas, optimize queries, and manage high-volume databases.
• SQL & Scripting: Strong SQL skills, plus familiarity with Python, Scala, or Java for data-related tasks.
• Soft Skills
• Excellent communication and team collaboration abilities.
• Strong problem-solving aptitude and analytical thinking.
• Detail-oriented, with a focus on delivering reliable, high-quality solutions.
• Preferred
• Experience in healthcare or imaging (e.g., DICOM, HL7/FHIR).
• Familiarity with DevOps tools (Docker, Kubernetes, Terraform) and CI/CD pipelines.
• Knowledge of machine learning workflows and MLOps practices.

The base salary range for this position is $120,000 - $180,000.  Our job postings cover multiple career levels and individual compensation will depend on various factors, including qualifications, skills, experience, location, and applicable laws. In addition to base salary, this role is eligible to participate in our equity incentive and competitive benefits plans. 

About Us

Nu is one of the largest digital financial platforms in the world, with more than 127 million customers across Brazil, Mexico, and Colombia. Guided by our mission to fight complexity and empower people, we are redefining financial services in Latin America and this is still just the beginning of the purple future we're building.

Listed on the New York Stock Exchange (NYSE: NU), we combine proprietary technology, data intelligence, and an efficient operating model to deliver financial products that are simple, accessible, and human.

Our impact has been recognized by global rankings such as Time 100 Companies, Fast Company's Most Innovative Companies, and Forbes World's Best Bank. Visit our institutional page [Careers at Nu - Join our team!](https://international.nubank.com.br/careers/)

About the Role

At Nu, data is the foundation that powers our AI and machine learning models, enabling millions of customers to access fair financial products. As a Machine Learning Engineer in AI Core, Data Intelligence, you’ll work across a broad spectrum — from building scalable data infrastructure and feature pipelines that feed our state-of-the-art foundation models to designing, training, and shipping transaction classification models that power critical customer experiences across the company.

You'll work at the intersection of data and applied machine learning, contributing across multiple stages of the ML lifecycle: ingesting and labeling data, training and evaluating models, and helping with deployment and production monitoring through robust quality controls. You’ll partner closely with product, compliance, and ML teams to ensure models are auditable, privacy-aware, and deliver measurable business value.

You'll join a team that manages the data engineering backbone of AI Core, ensuring data is accessible, healthy, and properly tracked across our entire ML ecosystem. Here, you'll combine your expertise in building scalable data systems with your passion for machine learning, creating solutions that enable our models to learn from better, richer data.

You can read more about the work in the AI Core team on our blog: https://building.nubank.com/understanding-our-customers-finances-through-foundation-models/

Key Responsibilities

As a Lead Machine Learning Engineer in AI Core Data Intelligence, you will:

• Design and build scalable data ingestion pipelines that bring new datasets into our AI Core platform, ensuring reliable, efficient data flow from source to model training.
• Implement data quality monitoring and validation systems that catch issues before they impact model performance, maintaining the health of datasets across our ML ecosystem.
• Model new types of data into our foundation models.
• Analyze the impact of new data sources on existing models, conducting experiments to measure performance improvements and guide data acquisition decisions.
• Develop and maintain data preparation workflows that transform raw data into features ready for model training, working with distributed computing frameworks like Ray.
• Tune and optimize machine learning models when new datasets are integrated, applying hyperparameter optimization and evaluating model performance improvements.
• Collaborate with AI Core ML, Platform, and Infra teams to ensure seamless data flow across our ML infrastructure, from ingestion to model deployment.
• Lead technical initiatives that improve our data engineering practices, setting standards for data quality, pipeline reliability, and model-data integration.
• Mentor team members and contribute to hiring activities, helping build a strong and diverse team that drives innovation in AI infrastructure.

Basic Qualifications

• Typically 6+ years of experience in machine learning engineering, data engineering, or related fields with a strong track record of building production data and ML systems.
• Proven experience designing and building data ingestion pipelines at scale, with expertise in distributed computing frameworks (Ray, Spark, or similar).
• Strong background in applied machine learning, including model training, hyperparameter tuning, and performance evaluation.
• Experience analyzing how data changes impact model performance, with the ability to design and run experiments to measure improvements.
• Proficiency in Python for data engineering and ML workflows, with experience working with large-scale data processing systems.
• Solid understanding of data quality principles and experience implementing monitoring, validation, and alerting systems.
• Strong problem-solving skills with the ability to address complex, ambiguous problems requiring coordination across multiple teams.
• Excellent communication skills, capable of explaining technical concepts to both technical and non-technical stakeholders.
• Demonstrated leadership experience, including mentoring team members and contributing to technical decision-making.

Preferred Qualifications

• Experience with MLflow or similar model tracking and versioning systems.
• Knowledge of foundation models, fine-tuning workflows, and transformer architectures.
• Experience with data pipeline orchestration tools (Dagster, Airflow, or similar).
• Background in financial services or fintech, understanding the unique data challenges in this domain.
• Experience working in a fast-paced, high-growth environment with distributed teams.
• Track record of reducing complexity in data systems and improving developer experience for ML teams.
  
  

Our Benefits

• Opportunity of earning equity at Nu
• Medical Insurance
• Dental and Vision Insurance
• Life Insurance and AD&D
• Extended maternity and paternity leaves 
• Nucleo - Our learning platform of courses
• NuLanguage - Our language learning program
• NuCare - Our mental health and wellness assistance program
• Extended maternity and paternity leaves 
• 401K
• Saving Plans - Health Saving Account and Flexible Spending Account
• Work-from-home Allowance
• Relocation Assistance Package, if applicable.

Work Model for this Role

Hybrid 2-3 times/week: Our hybrid work model brings us to the office at least twice a week, on strategic days designed to maximize team connection and collaboration. For more details, visit https://building.nubank.com/nu-hybrid-work-model/

Locations: This role is available in any of our North American offices (Palo Alto, USA; Miami, USA; Durham, USA; Toronto, CAN)