Machine Learning Engineer Jobs at Affirm with Visa Sponsorship
Machine Learning Engineer jobs at Affirm involve building responsible credit and real-time decisioning infrastructure, focusing on fraud detection, underwriting models, and personalization at scale. Affirm has a consistent track record of sponsoring international engineers across multiple visa categories.
Find Machine Learning Engineer Jobs at AffirmOverview
Showing 5 of 5+ Machine Learning Engineer Jobs at Affirm
Have you applied for this role?
Have you applied for this role?
Have you applied for this role?
Have you applied for this role?
Have you applied for this role?
See all Machine Learning Engineer Jobs at Affirm
Sign up for free to unlock all listings, filter by visa type, and get alerts for new Machine Learning Engineer Jobs at Affirm.
Get Access To All JobsINTRODUCTION
Affirm is reinventing credit to make it more honest and friendly, giving consumers the flexibility to buy now and pay later without any hidden fees or compounding interest.
On the Servicing ML team, you will build and improve machine learning and AI systems that automate customer operations such as disputes, returns, fraud, and chargebacks to make the best decisions for Affirm and our customers. You will work closely with experienced ML engineers, platform partners, and cross-functional stakeholders to take models from idea to prototype to production, and to keep them healthy with strong measurement and monitoring.
ROLE AND RESPONSIBILITIES
-
You will develop AI systems that automate dispute and chargeback handling using structured evidence and business logic, creating a better experience for our customers.
-
You will build models that automate refunds, getting money back to our customers faster.
-
You will build and maintain evidence extraction pipelines that process unstructured data using LLM-powered workflows to produce structured, actionable outputs.
-
You will prototype new modeling ideas, run offline experiments, and drive the best-performing approaches into production with appropriate risk controls.
-
You will collaborate across Engineering, Servicing Operations, Product, and ML Platform to define requirements, evaluate tradeoffs, and communicate results clearly to both technical and non-technical audiences.
BASIC QUALIFICATIONS
-
You have a total of 2+ years of experience as a machine learning engineer.
-
Strong Python skills and experience writing production-quality code.
-
Experience building and evaluating models for tabular classification problems (preferably gradient-boosted decision trees like LightGBM/XGBoost/CatBoost).
-
Experience building applications with LLM APIs (e.g., OpenAI, Anthropic), including structured extraction, prompt engineering, and orchestration frameworks like LangChain or LangGraph.
-
Familiarity with document and unstructured data processing (PDF/image extraction, text parsing, or similar).
-
Experience with ML lifecycle tooling for training orchestration, experimentation, and model monitoring (e.g., Kubeflow, Airflow, MLflow, or equivalent internal platforms).
-
Proficient in using AI-powered developer tools (e.g., Claude Code, Cursor, or similar) to accelerate iteration, debugging, and code quality as part of day-to-day development workflows.
-
You have mastered taking a simple problem or business scenario into a solution that interacts with multiple software components, and executing on it by writing clear, easily understood, well tested and extensible code.
-
You are comfortable navigating a large code base, debugging others' code, and providing feedback to other engineers through code reviews.
-
Your experience demonstrates that you take ownership of your growth, proactively seeking feedback from your team, your manager, and your stakeholders.
-
You have strong verbal and written communication skills that support effective collaboration with our global engineering team.
-
This position requires either equivalent practical experience or a Bachelor's degree in a related field.
PREFERRED QUALIFICATIONS
Base Pay Grade - L
Equity Grade - 6
Employees new to Affirm typically come in at the start of the pay range. Affirm focuses on providing a simple and transparent pay structure which is based on a variety of factors, including location, experience and job-related skills.
Base pay is part of a total compensation package that may include equity rewards, monthly stipends for health, wellness and tech spending, and benefits (including 100% subsidized medical coverage, dental and vision for you and your dependents.)
USA base pay range (CA, WA, NY, NJ, CT) per year: $160,000 - $210,000
USA base pay range (all other U.S. states) per year: $142,000 - $192,000
WORK ARRANGEMENT
LI-Remote
Affirm is proud to be a remote-first company! The majority of our roles are remote and you can work almost anywhere within the country of employment. Affirmers in proximal roles have the flexibility to work remotely, but will occasionally be required to work out of their assigned Affirm office. A limited number of roles remain office-based due to the nature of their job responsibilities.
We're extremely proud to offer competitive benefits that are anchored to our core value of people come first. Some key highlights of our benefits package include:
-
Health care coverage - Affirm covers all premiums for all levels of coverage for you and your dependents.
-
Flexible Spending Wallets - generous stipends for spending on Technology, Food, various Lifestyle needs, and family forming expenses.
-
Time off - competitive vacation and holiday schedules allowing you to take time off to rest and recharge.
-
ESPP - An employee stock purchase plan enabling you to buy shares of Affirm at a discount.
We believe It's On Us to provide an inclusive interview experience for all, including people with disabilities. We are happy to provide reasonable accommodations to candidates in need of individualized support during the hiring process.
Pursuant to the San Francisco Fair Chance Ordinance and Los Angeles Fair Chance Initiative for Hiring Ordinance, Affirm will consider for employment qualified applicants with arrest and conviction records.
By clicking "Submit Application," you acknowledge that you have read Affirm's Global Candidate Privacy Notice and hereby freely and unambiguously give informed consent to the collection, processing, use, and storage of your personal information as described therein.
INTRODUCTION
Affirm is reinventing credit to make it more honest and friendly, giving consumers the flexibility to buy now and pay later without any hidden fees or compounding interest.
On the Servicing ML team, you will build and improve machine learning and AI systems that automate customer operations such as disputes, returns, fraud, and chargebacks to make the best decisions for Affirm and our customers. You will work closely with experienced ML engineers, platform partners, and cross-functional stakeholders to take models from idea to prototype to production, and to keep them healthy with strong measurement and monitoring.
ROLE AND RESPONSIBILITIES
-
You will develop AI systems that automate dispute and chargeback handling using structured evidence and business logic, creating a better experience for our customers.
-
You will build models that automate refunds, getting money back to our customers faster.
-
You will build and maintain evidence extraction pipelines that process unstructured data using LLM-powered workflows to produce structured, actionable outputs.
-
You will prototype new modeling ideas, run offline experiments, and drive the best-performing approaches into production with appropriate risk controls.
-
You will collaborate across Engineering, Servicing Operations, Product, and ML Platform to define requirements, evaluate tradeoffs, and communicate results clearly to both technical and non-technical audiences.
BASIC QUALIFICATIONS
-
You have a total of 2+ years of experience as a machine learning engineer.
-
Strong Python skills and experience writing production-quality code.
-
Experience building and evaluating models for tabular classification problems (preferably gradient-boosted decision trees like LightGBM/XGBoost/CatBoost).
-
Experience building applications with LLM APIs (e.g., OpenAI, Anthropic), including structured extraction, prompt engineering, and orchestration frameworks like LangChain or LangGraph.
-
Familiarity with document and unstructured data processing (PDF/image extraction, text parsing, or similar).
-
Experience with ML lifecycle tooling for training orchestration, experimentation, and model monitoring (e.g., Kubeflow, Airflow, MLflow, or equivalent internal platforms).
-
Proficient in using AI-powered developer tools (e.g., Claude Code, Cursor, or similar) to accelerate iteration, debugging, and code quality as part of day-to-day development workflows.
-
You have mastered taking a simple problem or business scenario into a solution that interacts with multiple software components, and executing on it by writing clear, easily understood, well tested and extensible code.
-
You are comfortable navigating a large code base, debugging others' code, and providing feedback to other engineers through code reviews.
-
Your experience demonstrates that you take ownership of your growth, proactively seeking feedback from your team, your manager, and your stakeholders.
-
You have strong verbal and written communication skills that support effective collaboration with our global engineering team.
-
This position requires either equivalent practical experience or a Bachelor's degree in a related field.
PREFERRED QUALIFICATIONS
Base Pay Grade - L
Equity Grade - 6
Employees new to Affirm typically come in at the start of the pay range. Affirm focuses on providing a simple and transparent pay structure which is based on a variety of factors, including location, experience and job-related skills.
Base pay is part of a total compensation package that may include equity rewards, monthly stipends for health, wellness and tech spending, and benefits (including 100% subsidized medical coverage, dental and vision for you and your dependents.)
USA base pay range (CA, WA, NY, NJ, CT) per year: $160,000 - $210,000
USA base pay range (all other U.S. states) per year: $142,000 - $192,000
WORK ARRANGEMENT
LI-Remote
Affirm is proud to be a remote-first company! The majority of our roles are remote and you can work almost anywhere within the country of employment. Affirmers in proximal roles have the flexibility to work remotely, but will occasionally be required to work out of their assigned Affirm office. A limited number of roles remain office-based due to the nature of their job responsibilities.
We're extremely proud to offer competitive benefits that are anchored to our core value of people come first. Some key highlights of our benefits package include:
-
Health care coverage - Affirm covers all premiums for all levels of coverage for you and your dependents.
-
Flexible Spending Wallets - generous stipends for spending on Technology, Food, various Lifestyle needs, and family forming expenses.
-
Time off - competitive vacation and holiday schedules allowing you to take time off to rest and recharge.
-
ESPP - An employee stock purchase plan enabling you to buy shares of Affirm at a discount.
We believe It's On Us to provide an inclusive interview experience for all, including people with disabilities. We are happy to provide reasonable accommodations to candidates in need of individualized support during the hiring process.
Pursuant to the San Francisco Fair Chance Ordinance and Los Angeles Fair Chance Initiative for Hiring Ordinance, Affirm will consider for employment qualified applicants with arrest and conviction records.
By clicking "Submit Application," you acknowledge that you have read Affirm's Global Candidate Privacy Notice and hereby freely and unambiguously give informed consent to the collection, processing, use, and storage of your personal information as described therein.
See all Machine Learning Engineer Jobs at Affirm
Sign up for free to unlock all listings, filter by visa type, and get alerts for new Machine Learning Engineer Jobs at Affirm.
Get Access To All JobsTips for Finding Machine Learning Engineer Jobs at Affirm
Align your portfolio to financial ML use cases
Affirm's ML teams focus on credit risk modeling, fraud detection, and real-time inference. Projects or papers demonstrating experience with imbalanced datasets, time-series financial data, or low-latency prediction systems will read as directly relevant to their hiring criteria.
Confirm sponsorship eligibility before applying
Affirm sponsors H-1B, TN, and OPT candidates for ML roles, but not every req is approved for all visa types. Ask your recruiter early which categories the specific role supports so you don't reach offer stage with a mismatch.
Use Migrate Mate to surface open ML roles at Affirm
Affirm's ML openings span multiple teams and aren't always grouped clearly on their careers page. Migrate Mate filters verified sponsorship-eligible ML roles at Affirm so you can track current openings without manually sifting through postings.
Prepare your OPT or CPT documentation before the offer
F-1 candidates should have their DSO confirm OPT authorization timelines before accepting an offer. Affirm's onboarding moves quickly, and delays in USCIS EAD card delivery have caused start-date friction for ML hires in the past.
Understand how PERM timing affects your green card path
For EB-2 or EB-3 sponsorship, Affirm must complete a DOL PERM labor certification before filing your immigrant petition. PERM audits currently add months to the process, so start this conversation during negotiation, not after your first performance review.
Demonstrate production ML experience, not just research
Affirm's ML interviews heavily test system design and MLOps fluency alongside modeling depth. Candidates who can speak to deploying models in production environments, monitoring for data drift, and owning model pipelines tend to progress faster through their technical screens.
Frequently Asked Questions
Does Affirm sponsor H-1B visas for Machine Learning Engineers?
Yes, Affirm sponsors H-1B visas for Machine Learning Engineer roles. Their engineering organization has consistently supported H-1B filings for technical positions, including ML-focused roles. If you're subject to the H-1B lottery, confirm with your recruiter early that the specific role is approved for sponsorship, since not every open req automatically qualifies.
How do I apply for Machine Learning Engineer jobs at Affirm?
You can apply directly through Affirm's careers page or find sponsorship-eligible ML openings filtered by visa type on Migrate Mate. Before applying, review the role's technical focus area, since Affirm's ML teams are split across credit, fraud, growth, and platform functions. Tailoring your resume to the specific team's domain improves your chances of clearing the initial screen.
Which visa types does Affirm commonly use for Machine Learning Engineer roles?
Affirm sponsors H-1B, F-1 OPT, F-1 CPT, TN visa, and EB-2/EB-3 Green Card pathways for ML engineering roles. F-1 OPT is commonly used for recent graduates starting immediately, while H-1B is the standard long-term work visa for this function. TN visa is available to Canadian and Mexican nationals in qualifying technical classifications.
What qualifications does Affirm expect from Machine Learning Engineer candidates?
Affirm generally looks for a bachelor's or master's degree in computer science, statistics, or a related quantitative field, along with hands-on experience building and deploying ML models in production. Strong candidates demonstrate familiarity with Python, distributed data systems, and at least one of Affirm's core ML domains: credit risk, fraud detection, or personalization. Research publications are a plus but not required.
How long does the H-1B sponsorship process take at a company like Affirm?
For cap-subject H-1B filings, the USCIS lottery runs in March each year with an October 1 start date for selected petitions. Premium processing is available and reduces USCIS adjudication to 15 business days once a petition is filed. If you're transferring an existing H-1B to Affirm, you can typically start under portability rules as soon as USCIS receives the transfer petition.