Interested in this AI/ML Engineer role at Upstart?
Apply Now →Skills & Technologies
About This Role
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 Applied LLM team is building foundational infrastructure that democratizes access to generative AI for every product and engineering team across the company. This is a cross\-functional team at the intersection of machine learning, product, and engineering. Our mission is to bring the power of ML, particularly large language models (LLMs) and generative AI, to life in Upstart’s core products.
As a Senior Applied Machine Learning Engineer focused on building Upstart's LLM applications, you'll work closely with researchers, product managers, platform engineers, and designers to ship intelligent features that elevate the user experience and expand the capabilities of our systems.
How you’ll make an impact:
- Design and build user\-facing ML features that harness LLMs and generative AI to unlock new product capabilities
- Partner with product, design, and ML research to prototype and deliver high\-impact, ML\-powered experiences
- Own the technical architecture and implementation strategy for applied ML systems \- balancing latency, observability, and iteration speed
- Build scalable services and APIs that bring model outputs to users in trustworthy and intuitive ways
- Collaborate across platform, infra, and legal/compliance teams to ensure ML deployments meet standards for safety, fairness, and performance
- Establish and evangelize best practices for prompt design, model evaluation, and experimentation across the org
What we’re looking for:
- Minimum qualifications:
- + 4\+ years of software engineering experience, with 2\+ years working directly on ML\-driven products or intelligent systems
+ Proven ability to lead complex initiatives across engineering, product, and research stakeholders
+ Strong backend development skills (e.g., Python with FastAPI or Flask), plus experience with cloud\-native tooling (e.g., Kubernetes, Docker, Terraform)
+ Experience integrating LLMs or ML models into production systems, including APIs and user\-facing applications
+ Excellent communication skills and a collaborative, product\-minded approach
Ability to think rigorously about system design, latency tradeoffs, and user impact when working with ML features
+
- Preferred qualifications:
- + Experience shipping GenAI or LLM\-powered features using frameworks like LangChain, LlamaIndex, or OpenAI APIs
+ Familiarity with retrieval\-augmented generation (RAG), vector search (e.g., FAISS, Pinecone), and real\-time inference patterns
+ Proficiency in full\-stack development, including front\-end work with React or similar frameworks
+ Strong intuition for prompt engineering, model testing, and evaluation methodologies
+ Experience navigating complex requirements around explainability, user trust, or compliance in ML applications
+ Track record of influencing architecture or product direction at a team or org level
Position location This role is available in the following locations: Remote
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 but are encouraged to to still 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.
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
$167,700—$231,800 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
$159,000—$193,000 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
- 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 $159K-$231K range is above the median for AI/ML Engineer roles in our dataset (median: $175K across 2162 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Upstart, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $214,900 based on 6,420 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($195K) sits 9% below the category median. Disclosed range: $159K to $231K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Upstart AI Hiring
Upstart has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US. Compensation range: $231K - $231K.
Remote Work Context
Remote AI roles pay a median of $180,000 across 1,196 positions. About 15% of all AI roles offer remote work.
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 Safety roles lead at $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.