The role
From Upstart's own posting.
About Upstart
At Upstart, we’re united by a mission that matters: to radically reduce the cost and complexity of borrowing for all Americans. Every day, we bring creativity, experimentation, and advanced AI to reshape access to credit, helping millions move forward financially with clarity and confidence.
As the leading AI lending marketplace, we partner with banks and credit unions to expand access to affordable credit through technology that’s both radically intelligent and deeply human. Our platform runs over one million predictions per borrower using more than 3,000 signals, powering smarter, fairer decisions for millions of customers. But the numbers only hint at the impact. Every idea, every voice, and every contribution moves us closer to a world where credit never stands between people and their financial progress.
We’re proudly digital-first, giving most Upstarters the flexibility to do their best work from wherever they thrive, alongside teammates across 80+ cities in the US and Canada. Digital-first doesn’t mean distant. We’re intentional about in-person connection through team onsites, planning sessions, and moments that spark creativity and trust. And whether you choose to work primarily from home or collaborate in-person from one of our offices in Columbus, Austin, the Bay Area, or New York City, you’ll have the support to work in the way that works best for you.
If you’re energized by tackling meaningful problems, excited to innovate with purpose, and motivated by work that truly matters, we’d love to hear from you.
The Team
Upstart’s ML Data Enablement team is a platform team with end-to-end ownership (from source to inference) of the data lifecycle that powers all ML models across external vendors and internal datasets. The team’s mission is to make it dramatically easier for ML teams to discover, evaluate, trust, and productionize high-impact data. — with particular emphasis on accelerating new third-party data onboarding and unlocking under-leveraged internal data.
The team builds scalable infrastructure, standardized workflows, and quality guarantees that reduce integration time, increase evaluation velocity, and enforce strong ownership and SLAs across the ML data lifecycle.
As the Sr. Engineering Manager - ML Data Enablement, you will lead this organization and define the strategy, operating model, and execution roadmap that increases data evaluation velocity and reduces time-to-production for high-value data sources. You will partner cross-functionally with ML, ML Platform, Procurement, Data Platform, and product engineering teams to transform data from a bottleneck into a durable competitive advantage.
How you’ll make an impact
Build and lead a high-performing team spanning data integration, data quality, metadata, and ML-critical data infrastructure for online inference and offline training , including standing up new dedicated integration capacity where needed.
Set and execute the technical strategy aligned to measurable north star metrics such as increasing data evaluation velocity and reducing time to production.
Drive robust data quality and reconciliation frameworks, including retro vs. production checks, ingress-level monitoring, and drift detection to prevent launch issues and downstream model degradation.
Champion a company-wide shift toward data contracts and SLAs, ensuring data producers adopt clear ownership, quality standards, and monitoring practices for ML-critical datasets.
Establish clear end-to-end ownership across the third-party and internal data lifecycle, eliminating fragmented workflows and implicit accountability.
Accelerate third-party data onboarding by operationalizing standardized vendor intake, secure retro ingestion, templated integrations, and configurable microservices that reduce engineering lift and cycle time.
Unlock internal data for ML innovation by improving metadata coverage, lineage standards, ownership contracts, and ML discoverability across high-impact internal domains
What we’re looking for
Minimum requirements
Bachelor’s degree in Computer Science, Engineering, or Mathematics, or a related field (or its equivalent) + 8 years of engineer experience, including at least 3 years of direct people management experience
Owned production data pipelines that enable both offline training and online inference
Proven experience building and scaling data systems in modern stacks (e.g., Databricks/Spark, Python, SQL, AWS, streaming systems, orchestration frameworks) and distributed systems architecture.
Demonstrated ownership of complex cross-functional initiatives spanning engineering, ML, and business stakeholders, including delivery under peer pushback and dependency negotiation.
Experience designing and enforcing data quality frameworks and observability for production systems, including reconciliation, drift detection, and incident/postmortem operating loops.
Preferred qualifications
10+ years in data engineering AND ML platform OR ML data platform roles , with 5+ years managing engineering teams. (strongly preferred(
Experience with feature stores and real-time feature delivery or equivalent feature transformation interfaces used in inference .
Strong knowledge of lakehouse architecture and big data processing frameworks.
Familiarity with DevOps and infrastructure-as-code practices (Kubernetes, Terraform, CI/CD).
Experience in fintech or other regulated environments where explainability, auditability, and controls matter.
Ability to translate complex technical tradeoffs into business impact and influence cross-functional strategy.
At Upstart, your base pay is one part of your total compensation package. The anticipated base salary for this position is expected to be within the below range. Your actual base pay will depend on your geographic location–with our “digital first” philosophy, Upstart uses compensation regions that vary depending on location. Individual pay is also determined by job-related skills, experience, and relevant education or training. Your recruiter can share more about the specific salary range for your preferred location during the hiring process.
In addition, Upstart provides employees with target bonuses, equity compensation, and generous benefits packages (including medical, dental, vision, and 401k).
United States | Remote - Anticipated Base Salary Range
$195,000 — $270,000 USD
What you'll love
At Upstart, our benefits are designed to support your health, financial well-being, family, and personal growth. Here’s what you can expect:
Competitive compensation, including base pay, bonus opportunities, and annual equity grants that vest quarterly
Retirement benefits to help you plan for the future, including a 401(k) or Group Retirement Savings Plan with a company match of $2 for every $1 contributed, up to $15,000 annually (USD in the US, CAD in Canada)
Employee Stock Purchase Plan (ESPP) with discounted stock purchase options for eligible employees (US only)
Comprehensive health coverage designed to support you and your family, including medical, dental, vision, and wellness resources for US and supplemental health coverage for Canada.
Health Savings Account contributions from Upstart for eligible plans (US only)
Income protection benefits, including life insurance and disability coverage for added financial security
Paid time off, sick leave, and company holidays, in line with local requirements
Paid family and parental leave to support caregiving and major life moments (duration varies by country)
Family-centered benefits to support fertility, parenthood, and caregiving needs
Employee Assistance Program (EAP) offering mental health support and life-centered resources
Financial wellness resources, including access to financial planning tools and a financial concierge service (US Only)
Annual wellness allowance to support your physical and emotional well-being and personal development, based on what matters most to you
Annual productivity allowance to invest