[Remote] Staff/Principal Machine Learning Engineer

Note: The job is a remote job and is open to candidates in USA. Upstart is hiring a Staff/Principal Machine Learning Engineer for its Machine Learning Platform team. The role builds scalable infrastructure, tools, and workflows across the model lifecycle to accelerate model development and improve predictive accuracy, while setting technical direction and collaborating with applied ML and engineering teams.

Responsibilities

• 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
• 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
• Across all of these efforts, you will work backward from applied ML projects that meaningfully improve accuracy—using those real-world scenarios to reinvent or improve existing platform capabilities that enable ML teams across Upstart to innovate with greater speed, reliability, and impact
• 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

Skills

• 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
• 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

Benefits

• Target bonuses
• Equity compensation
• Annual equity grants that vest quarterly
• 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, including medical, dental, vision, and wellness resources for US employees, and supplemental health coverage for Canada
• Health Savings Account contributions from Upstart for eligible plans (US only)
• Life insurance and disability coverage
• Paid time off, sick leave, and company holidays, in line with local requirements
• Paid family and parental leave (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 planning tools and a financial concierge service (US only)
• Annual wellness allowance to support physical and emotional well-being and personal development
• Annual productivity allowance for relevant tools and resources
• Team events, all-company updates, and employee resource groups (ERGs)
• Catered lunches and fully stocked micro-kitchens when working from offices in the Bay Area, Austin, Columbus, and New York City
• Digital-first work flexibility, including the option to work primarily from home or from an office
• Most employees can live and work anywhere in the U.S.; teams are encouraged to meet in person, with most teams meeting once or twice per quarter for 2-4 consecutive days

Company Overview

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