- Страна
- США
- Зарплата
- 195 000 $ – 270 000 $
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Senior Engineering Manager - Machine Learning Data Enablement
Отличная позиция в ведущей AI-компании с прозрачной вилкой зарплаты, сильным соцпакетом (включая 401k match и акции) и возможностью удаленной работы. Роль предлагает высокий уровень ответственности и влияние на ключевой продукт компании.
Сложность вакансии
Высокая сложность обусловлена необходимостью глубоких знаний как в Data Engineering, так и в ML Platform, а также опытом управления командами более 5 лет в финтех-среде. Роль требует навыков ведения переговоров со стейкхолдерами и внедрения сложных архитектурных решений (Lakehouse, Feature Stores).
Анализ зарплаты
Предложенная вилка $195k–$270k полностью соответствует рыночным стандартам для Senior Engineering Manager в США, особенно в секторе Fintech и AI. Верхняя граница диапазона является весьма конкурентной даже для Tier-1 компаний.
Сопроводительное письмо
I am writing to express my strong interest in the Senior Engineering Manager - ML Data Enablement position at Upstart. With over a decade of experience in data engineering and a proven track record of managing high-performing teams, I am excited by the opportunity to lead the organization responsible for the end-to-end data lifecycle that powers Upstart’s industry-leading AI models. My background in building scalable infrastructure using Databricks, Spark, and AWS, combined with my experience in enforcing data contracts and quality frameworks, aligns perfectly with your mission to accelerate data evaluation velocity.
Throughout my career, I have successfully bridged the gap between complex data engineering and machine learning requirements, particularly in regulated environments where auditability and real-time inference are critical. I am particularly drawn to Upstart’s digital-first culture and your commitment to using AI to expand access to affordable credit. I am confident that my expertise in operationalizing third-party data onboarding and unlocking internal data assets will help transform Upstart’s data ecosystem into an even more durable competitive advantage.
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Описание вакансии
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 1,800 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 (opening Summer 2026), 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
- 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
- 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!).
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
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Навыки
- AWS
- Python
- Terraform
- SQL
- Kubernetes
- CI/CD
- Data Engineering
- Apache Spark
- Distributed Systems
- Databricks
- Feature Store
- Machine Learning Platform
- Lakehouse architecture
Возможные вопросы на собеседовании
Проверка опыта управления жизненным циклом данных для ML.
Опишите ваш опыт построения пайплайнов, которые одновременно поддерживают офлайн-обучение и онлайн-инференс. С какими основными проблемами согласованности данных вы сталкивались?
Оценка лидерских качеств и умения работать с сопротивлением.
Расскажите о случае, когда вам приходилось внедрять кросс-функциональную инициативу (например, контракты данных) при наличии сопротивления со стороны других команд. Как вы добились консенсуса?
Проверка технического видения в области качества данных.
Как бы вы спроектировали систему обнаружения дрейфа данных (drift detection) для высоконагруженной платформы кредитования, чтобы минимизировать деградацию моделей?
Оценка опыта работы с внешними данными.
Каков ваш подход к операционализации процесса подключения сторонних вендоров данных для сокращения времени вывода в продакшен (time-to-production)?
Проверка понимания современных архитектур.
В чем, по вашему мнению, заключаются главные преимущества и риски использования Lakehouse архитектуры по сравнению с традиционными Feature Stores в контексте ML-платформы?
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- Страна
- США
- Зарплата
- 195 000 $ – 270 000 $