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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 Growth and Marketplace Optimization teams are responsible for building the decision systems that determine how offers are presented, optimized, and evolved across the customer journey. These systems sit at the intersection of machine learning, optimization, pricing, marketplace dynamics, and borrower behavior. The work spans partner channels, onsite experiences, and marketplace optimization, with the goal of improving conversion, borrower experience, lender surplus, and overall marketplace efficiency. As these systems continue to evolve, the organization is investing in a more unified technical vision that connects decision\-making across stages rather than optimizing individual components in isolation.
The Role:
As a Principal Applied Scientist at Upstart, you will help define the long\-term technical direction for some of Upstart’s most important offer optimization and conversion modeling systems. You will work across multiple teams to define how conversion modeling, offer optimization, and system design should fit together, ensuring models and optimization systems account for downstream effects, marketplace constraints, and customer outcomes.
This role is intentionally broad and high leverage. You will help structure ambiguous problem spaces, design solutions that account for interactions across multiple stages of the customer journey, and provide technical oversight to ensure work across multiple teams converges toward a coherent long\-term vision. The work sits at the intersection of operations research, optimization, causal machine learning, and production decision systems.
How you’ll make an impact:
- Define the technical vision for how offer decisioning systems should interconnect across partnerships, always\-on systems, and marketplace optimization
- Build and guide conversion modeling approaches that optimize decisions across multiple stages of the customer journey rather than in isolated local steps
- Ensure models and decision policies at one stage account for downstream impacts, business constraints, and later\-stage optimization opportunities
- Design interfaces between decision systems and optimization or constraint\-specification components
- Drive cross\-functional technical alignment across teams that currently own adjacent pieces of the problem
- Scope and lead large, ambiguous initiatives that require both deep modeling judgment and strong systems thinking
- Partner with scientists, engineers, and cross\-functional stakeholders to translate analytical insights into durable production approaches
- Help ensure ongoing work across domains progresses toward a unified architecture and decisioning strategy
- Contribute directly to implementation and experimentation efforts, including prototyping models, reviewing code, and helping teams operationalize new approaches in production environments.
Minimum Qualifications:
- Advanced degree in a quantitative field such as statistics, mathematics, economics, computer science, operations research, or a related discipline
- 8\+ years of experience building and deploying machine learning models into production at scale
- Experience with optimization, operations research, or constrained decision\-making problems
- Working knowledge of causal inference or causal machine learning
- Strong grounding in statistics and probability
- Experience leading large cross\-functional technical initiatives with multiple stakeholders
- Experience working across multiple technical teams to align approaches, define interfaces, and move toward a shared vision
- Experience solving real\-world machine learning or data science problems in a high\-impact production environment
Preferred Qualifications:
- PhD in operations research, statistics, economics, computer science, or another quantitative field
- Experience with offer optimization, pricing systems, marketplace optimization, or related decisioning systems
- Experience with end\-to\-end modeling from problem framing through productionization
- Experience in fintech, lending, marketplaces, or other domains where decisions must account for downstream constraints and business tradeoffs
- Experience serving as a principal\-level technical lead across multiple teams or product areas
- Experience mentoring other senior scientists or raising the technical bar across an organization
Travel requirements As a digital first company, the majority of your work can be accomplished remotely. The majority of our employees can live and work anywhere in the U.S or Canada (outside of Quebec) but are expected to spend high quality time in\-person collaborating via regular onsites and in\-person meetings. The onsite cadence varies depending on the team and role; most teams meet once or twice per quarter for 2\-4 consecutive days at a time.
Position location This role is available in the following locations: Remote
Time zone requirements The team operates on the East/West coast time zones.
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
$238,400—$330,200 USD
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).
Canada \| Remote \- Anticipated Base Salary Range
$220,200—$277,000 CAD
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
- Retirement benefits to help you plan for the future, including a 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 designed to support you and your family, including 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, including life insurance and disability coverage for added financial security
- 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, 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, all\-company updates, and employee resource groups (ERGs)
- Onsite perks, including 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!)
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\[email protected]*
https://www.upstart.com/candidate\_privacy\_policy
Salary Context
This $220K-$330K range is above the 75th percentile for Research Scientist roles in our dataset (median: $196K across 93 roles with salary data).
Role Details
About This Role
Research Scientists push the boundaries of what AI can do. They design experiments, develop novel architectures, publish papers, and translate research breakthroughs into production capabilities. This is where the fundamental advances happen, from attention mechanisms to diffusion models to reasoning chains.
The work is intellectually demanding and often ambiguous. You might spend months on an approach that doesn't pan out. The best research scientists combine deep mathematical intuition with engineering pragmatism. They know when to go deep on theory and when to run experiments. They read papers voraciously and can spot incremental contributions from genuine breakthroughs.
Across the 3,824 AI roles we're tracking, Research Scientist positions make up 3% of the market. At Upstart, this role fits into their broader AI and engineering organization.
Research Scientist roles are concentrated at major AI labs (OpenAI, Anthropic, Google DeepMind, Meta FAIR) and well-funded AI startups. The competition is intense. PhD is effectively required for most positions, and publication track record matters. Compensation is among the highest in AI, reflecting both the scarcity of talent and the strategic importance of research breakthroughs.
What the Work Looks Like
A typical week includes: reading and discussing recent papers with your team, designing and running experiments on multi-GPU clusters, analyzing results and iterating on hypotheses, writing up findings for internal review or publication, and collaborating with engineering teams to productionize promising results. The ratio of thinking to coding is higher than in engineering roles.
Research Scientist roles are concentrated at major AI labs (OpenAI, Anthropic, Google DeepMind, Meta FAIR) and well-funded AI startups. The competition is intense. PhD is effectively required for most positions, and publication track record matters. Compensation is among the highest in AI, reflecting both the scarcity of talent and the strategic importance of research breakthroughs.
Skills in Demand for This Role
PhD strongly preferred for most roles. Deep expertise in a specific area (NLP, computer vision, reinforcement learning, multimodal) is expected. PyTorch is the standard. Publication track record matters. Strong mathematical foundations in linear algebra, probability, optimization, and information theory are assumed.
Beyond the fundamentals, companies value experience with large-scale distributed training, novel architecture design, and the ability to bridge theory and practice. Understanding of current frontier topics (reasoning, multimodal, long-context, alignment) is essential. Code quality matters more than many researchers expect. Labs want researchers who can implement their ideas cleanly.
Strong research postings specify the research area, mention the team you'd join, and describe the problems they're working on. They often list recent publications from the team. Vague 'AI research' postings without specifics usually mean the company wants to sound impressive but doesn't have a real research agenda.
Compensation Benchmarks
Research Scientist roles pay a median of $223,400 based on 223 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($275K) sits 23% above the category median. Disclosed range: $220K to $330K.
Across all AI roles, the market median is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Safety ($274,200). By seniority level: Entry: $97,380; Mid: $160,000; Senior: $227,400; Director: $243,000; VP: $250,000.
Upstart AI Hiring
Upstart has 2 open AI roles right now. They're hiring across Research Scientist, AI/ML Engineer. Based in Remote, US. Compensation range: $162K - $330K.
Remote Work Context
Remote AI roles pay a median of $169,035 across 1,817 positions. About 16% of all AI roles offer remote work.
Career Path
Common paths into Research Scientist roles include PhD Student, Research Engineer, Postdoc.
From here, career progression typically leads toward Research Lead, Distinguished Scientist, VP of Research.
The PhD is the entry point for most paths. Choose your advisor and research area carefully since they'll define your first industry position. Publish consistently, contribute to open-source projects in your area, and build relationships at conferences. Industry research offers better compensation and compute resources than academia, but the pressure to show product impact is real.
What to Expect in Interviews
Research interviews are multi-stage: a research talk (present your best paper), technical deep-dives on your methodology, and often a 'research proposal' exercise where you design an experiment to test a hypothesis. Coding rounds test implementation ability alongside theoretical knowledge. Be prepared to implement a paper from scratch and discuss the design choices the authors made. Strong candidates can critique papers constructively and identify gaps in experimental methodology.
When evaluating opportunities: Strong research postings specify the research area, mention the team you'd join, and describe the problems they're working on. They often list recent publications from the team. Vague 'AI research' postings without specifics usually mean the company wants to sound impressive but doesn't have a real research agenda.
AI Hiring Overview
The AI job market has 3,824 open positions tracked in our dataset. By seniority: 119 entry-level, 1,813 mid-level, 1,472 senior, and 420 leadership roles (Director, VP, C-Level). Remote roles make up 16% of the market (613 positions). The remaining 3,187 roles require on-site or hybrid attendance.
The market median for AI roles is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($293,500 median, 31 roles); AI Safety ($274,200 median, 51 roles); Research Engineer ($260,000 median, 401 roles).
Research Scientist roles are concentrated at major AI labs (OpenAI, Anthropic, Google DeepMind, Meta FAIR) and well-funded AI startups. The competition is intense. PhD is effectively required for most positions, and publication track record matters. Compensation is among the highest in AI, reflecting both the scarcity of talent and the strategic importance of research breakthroughs.
The AI Job Market Today
The AI job market spans 3,824 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,702), Data Scientist (281), AI Software Engineer (258). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (119) are outnumbered by mid-level (1,813) and senior (1,472) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 420 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 16% of all AI roles (613 positions), with 3,187 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $200,000. Top-quartile roles start at $253,000, and the 90th percentile reaches $307,500. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $142,800. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,968 postings), Aws (1,203 postings), Azure (882 postings), Rag (877 postings), Gcp (735 postings), Prompt Engineering (587 postings), Pytorch (586 postings), Claude (554 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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