Senior Engineering Manager - Machine Learning Data Enablement
Required skills
- production data pipelines
- offline training
- online inference
- data quality
- observability
- reconciliation
- drift detection
- incident management
- postmortem
- data contracts
Nice to have
- feature stores
- real-time feature delivery
- lakehouse architecture
- big data processing
- Kubernetes
- Terraform
Role summary
Upstart is seeking a Senior Engineering Manager for their ML Data Enablement team. This platform team owns the end-to-end data lifecycle for ML models, aiming to simplify data discovery, evaluation, trust, and productionization. The role involves leading a team focused on data integration, quality, metadata, and ML infrastructure. Responsibilities include defining strategy, building scalable infrastructure, driving data quality, championing data contracts and SLAs, and accelerating third-party data onboarding. The ideal candidate will have a Bachelor's degree in CS or related field, 8+ years of engineering experience with 3+ years in people management, and proven experience with production data pipelines and scaling data systems. Preferred qualifications include 10+ years in data engineering/ML platform roles and experience in regulated environments.
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
What we’re looking for
Minimum requirements
Preferred qualifications
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 USDWhat you'll love
At Upstart, our benefits for full-time employees are designed to support your health, financial well-being, family, and personal growth. Here’s what you can expect:
For roles based in Canada, please note that we are not currently able to hire in Quebec.
Upstart is a proud Equal Opportunity Employer. Just as we are dedicated to improving access to affordable credit for all, we are committed to inclusive and fair hiring practices.
If you require reasonable accommodation in completing an application, interviewing, completing any pre-employment testing, or otherwise participating in the employee selection process, please email candidate_accommodations@upstart.com
https://www.upstart.com/candidate_privacy_policy
Sample Upstart interview questions
- 1
Diameter of a Binary Tree Find the diameter of a binary tree. Input: root = [1,2] Output: 1 Explanation: The longest path is simply the single edge connecting the root node to its only child.
codingmedium - 2
Connected Components in an Undirected Graph Determine the number of connected components in an undirected graph. Input: n = 4, edges = [[0,1],[2,3]] Output: 2 Explanation: The graph is split completely evenly into two distinct isolated components: nodes {0,1} and nodes {2,3}.
codingmedium - 3
Split Array into Consecutive Subsequences Split an array into consecutive subsequences. Input: nums = [1,2,3,4,4,5] Output: FALSE Explanation: The numbers can form [1,2,3,4], but the remaining leftover group [4,5] is too short to form a valid sequence of length 3.
codingmedium - 4
Pacific and Atlantic Water Flow Calculate water flow from a matrix to the Pacific and Atlantic oceans. Input: heights = [[2,1],[1,2]] Output: [[0,0],[0,1],[1,0],[1,1]] Explanation: All cells can flow to both oceans because water can move to adjacent cells of equal or lower height or directly off the edges.
codingmedium - 5
Unique Combinations that Sum to a Target Find all unique combinations in an array that sum to a target. Input: candidates = [2,4,6], target = 6 Output: [[2,2,2], [2,4], [6]] Explanation: Uses backtracking to find all valid combinations that sum to 6, allowing for explicitly repeated elements.
codingmedium
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