Senior Engineering Manager - Machine Learning Data Enablement

Upstart

7d•Hybrid

About The Position

At Upstart, the mission is to radically reduce the cost and complexity of borrowing for all Americans by leveraging creativity, experimentation, and advanced AI to reshape access to credit. As a leading AI lending marketplace, Upstart partners with banks and credit unions to expand access to affordable credit through technology that makes smarter, fairer decisions for millions of customers. The company operates on a digital-first model, offering flexibility for most employees to work remotely while fostering in-person connections through team onsites and planning sessions. Offices are located in Columbus, Austin, the Bay Area, and New York City (opening Summer 2026). The ML Data Enablement team is a platform team with end-to-end ownership of the data lifecycle, from source to inference, powering all ML models. Its mission is to significantly ease the process for ML teams to discover, evaluate, trust, and productionize high-impact data, with a focus on accelerating new third-party data onboarding and unlocking under-leveraged internal data. The team develops scalable infrastructure, standardized workflows, and quality guarantees to reduce integration time, increase evaluation velocity, and ensure strong ownership and SLAs across the ML data lifecycle. The Senior Engineering Manager - ML Data Enablement will lead this organization, defining strategy, operating model, and execution roadmap to enhance data evaluation velocity and reduce time-to-production for high-value data sources. This role involves cross-functional collaboration with ML, ML Platform, Procurement, Data Platform, and product engineering teams to transform data into a competitive advantage.

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.

Nice To Haves

10+ years in data engineering AND ML platform OR ML data platform roles, with 5+ years managing engineering teams.
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.

Responsibilities

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.

Benefits

Competitive compensation, including base pay, bonus opportunities, and annual equity grants that vest quarterly.
Generous 401(k) plan with Upstart matching $2 for every $1 contributed, up to $15,000 per year.
Employee Stock Purchase Plan (ESPP) with discounted stock purchase options for eligible employees.
Affordable medical, dental, and vision coverage, with multiple plan options - Upstart covers 90% to 100% of the cost depending on the plans you choose.
Health Savings Account contributions from Upstart for eligible plans.
Income protection benefits, including company-paid Basic Life, AD&D, and Short- and Long-Term Disability coverage, with options to purchase supplemental coverage.
Paid time off, sick and safe time, and company holidays.
Paid family and parental leave to support caregiving and major life moments.
Family-centered benefits through Carrot and Cleo, supporting fertility, parenthood, and caregiving.
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.
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 in relevant tools and resources you need to do your best work, no matter where you work from.
Connection and community through team events and onsites, all-company updates, and employee resource groups (ERGs).
Onsite perks, including catered lunches and fully stocked micro-kitchens when working from one of our four offices, located in the Bay Area, Austin, Columbus, and New York City (opening Summer 2026!).