Staff/Principal Machine Learning Engineer

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.

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. 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.

The Team

The Machine Learning Platform team builds the foundational technology that scales machine learning innovation across Upstart. As a Principal Machine Learning Engineer, you will work at the intersection of applied ML and platform engineering—collaborating closely with Research Scientists, Data Scientists, and ML Platform Engineers to design tools and systems that accelerate model development to ultimately improve predictive accuracy. Success in this role requires a strong grasp of ML fundamentals and statistics and deep knowledge of the entire modeling lifecycle - from data preparation to training and deployment to production.

In this role, you will lead engineering initiatives that turn high-impact modeling needs into scalable, reusable infrastructure. This includes building a unified embeddings platform for training, serving, and managing representations at scale; streamlining feature engineering pipelines to reduce manual steps and deliver new signals quickly; developing automated continuous-learning systems that handle data refresh, retraining, evaluation, and drift monitoring with minimal manual effort; and scaling our training pipelines to support larger datasets, more complex architectures, and faster experimentation.

Responsibilities

• Scale ML innovation by building tools, infrastructure, and workflows that dramatically improve the speed and reliability of model development.
• Work backward from modeling needs to design systems that directly unlock gains in accuracy, efficiency, and scientific productivity.
• Explore new algorithms and methodologies for our machine learning models and develop tooling to support them.
• Improve the entire ML lifecycle—from data readiness and feature development through training, evaluation, serving, and monitoring.
• Automate and standardize operational workflows, enabling scientists to focus on high-leverage modeling and analysis rather than manual pipelines.
• Define the roadmap for our next generation ML Platform, balancing near-term impact with long-term architectural scalability.
• Collaborate cross-functionally with Data Engineering, ML Platform, Pricing, and other teams to build reliable, end-to-end ML systems.

Your work will multiply the effectiveness of every ML team at Upstart—accelerating innovation and advancing our mission to make credit more accurate, accessible, and fair. This is a high-influence role suited for those who enjoy combining science innovation with cross-functional collaboration and advisory.

Requirements

• Strong theoretical and practical foundation in machine learning and statistics.
• Ability to reason from first principles about model assumptions, sources of bias, uncertainty, tradeoffs, evaluation, and failure modes.
• A deep understanding of how models work beyond the abstractions provided by common tools and frameworks, and how to apply this knowledge to production solutions.
• 5-7+ years of hands-on experience in applied machine learning, with strong exposure to production-scale modeling efforts.
• Experience working in high-scale, ML-driven product environments—especially in fintech, pricing, or risk modeling.
• Proficiency in Python and core ML frameworks (e.g., PyTorch, TensorFlow, Scikit-learn, XGBoost).
• Ability to work autonomously and lead technical direction in ambiguous, high-impact domains.
• Experience collaborating with cross-functional teams including ML scientists, engineers, and product partners.
• Ability to bridge engineering and science teams, and influence technical strategy across disciplines.
• Numerically-savvy and smart with ability to operate at a fast pace.
• Master’s degree or PhD in a quantitative discipline, or equivalent additional professional experience.
• Demonstrated expertise in end-to-end model development: data prep, feature engineering, training, evaluation, and deployment.

Preferred Qualifications

• Practical experience optimizing ML workflows using CUDA/GPU acceleration.
• Background in feature store design, embedding architecture, or synthetic data generation for model training.
• Proven track record of improving model accuracy in production environments with measurable business outcomes.
• Familiarity with modern experimentation frameworks, hyperparameter tuning tools, and automated model selection techniques.

Schedule

The team operates on the East/West coast time zones.

As a digital-first company, the majority of your work can be accomplished remotely. Most employees can live and work anywhere in the U.S but are encouraged to spend high-quality time in-person collaborating via regular onsites. The in-person sessions’ cadence varies depending on the team and role; most teams meet once or twice per quarter for 2-4 consecutive days at a time.

Pay

The anticipated base salary for this position is expected to be within the range of $220,700 - $300,000 USD. Your actual base pay will depend on your geographic location, job-related skills, experience, and relevant education or training.

In addition, Upstart provides employees with target bonuses, equity compensation, and generous benefits packages (including medical, dental, vision, and 401k).

Benefits

• Competitive compensation, including base pay, bonus opportunities, and annual equity grants that vest quarterly.
• Retirement benefits: 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: 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: life insurance and disability coverage.
• 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.
• Annual productivity allowance to invest in relevant tools and resources you need to do your best work.
• Connection and community through team events, all-company updates, and employee resource groups (ERGs).
• Onsite perks: catered lunches and fully stocked micro-kitchens when working from one of our offices in the Bay Area, Austin, Columbus, and New York City (opening Summer 2026!).