Jobs · Information Technology

Machine Learning Engineer II (Underwriting ML)

Affirm · Chicago, IL · 4 wk ago
RemoteRemoteInformation Technology$165k–$225k/yrFull-time
We need to output HTML fragment with sections: headings using

for sections like About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule. Only include sections that have real specific content. We need to parse the job posting. Content: - Intro paragraph about Affirm and Underwriting ML team. Could be "About the role" maybe. - "What You’ll Do" list: bullet points. - "What We Look For": list of qualifications/experience. - Then "This position requires either equivalent practical experience or a Bachelor’s degree in a related field" maybe part of qualifications. - Then "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: $165,000 - $225,000 USA base pay range (all other U.S. states) per year: $146,000 - $206,000" - Then "Remote-first with flexibility built in Affirm is proud to be a remote-first company. Most roles can be done from almost anywhere within the country of employment. Some positions may occasionally require in-person work at an Affirm office, and a few are office-based due to the nature of the work. All new hires will be invited to attend an in-person onboarding experience." - Then "Benefits Designed For You Our benefits reflect our commitment to care, transparency, and flexibility. Here are a few highlights: Health coverage at no cost: We cover 100% of premiums for employees and their dependents.Spending stipends: Monthly stipends support your tech setup, and the ability to choose health and wellness options that are right for you.Time off to recharge: Flexible time off and generous holiday calendars help you rest when you need to.Own a piece of what you build: Our employee stock purchase plan (ESPP) lets you buy Affirm stock at a discount. We’re committed to providing an inclusive interview process, including accommodations for candidates with disabilities. If you need support, we’re happy to help. For positions based in San Francisco or Los Angeles: Affirm considers qualified applicants with arrest and conviction records, as required by law. By clicking "Submit Application," you acknowledge that you have read Affirm's Global Candidate Privacy Notice and consent to the use of your personal information as described." We need to remove boilerplate: cookie/navigation/legal/EEO/equal-opportunity/scam-warning boilerplate, duplicated lines, and application instructions. The last part about clicking submit application is application instructions; remove. Also the part about privacy notice maybe legal; remove. Also the part about positions based in SF or LA regarding arrest records maybe EEO? Might be considered EEO; but it's a specific statement about considering applicants with arrest and conviction records as required by law. That might be considered EEO/legal; we should remove. Also the inclusive interview process statement maybe also EEO; remove. So we keep benefits highlights but not the legal bits. We need sections: About the role (intro), Responsibilities (What You’ll Do), Requirements/Qualifications/Skills (What We Look For + degree requirement), Pay (pay ranges and equity grade etc), Benefits (benefits highlights), Schedule (remote-first flexibility). Also maybe we could combine Requirements and Qualifications into one section; but we need to use headings exactly as given: About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule. We should only include those that have real specific content. We can include About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule. But we need to ensure we don't create empty sections. Let's see if we have content for each. - About the role: intro paragraph about Affirm and Underwriting ML team. Yes. - Responsibilities: "What You’ll Do" bullet list. Yes. - Requirements: maybe the degree requirement and experience? Could be "This position requires either equivalent practical experience or a Bachelor’s degree in a related field". Also maybe the 2+ years experience etc. But those are in "What We Look For". Could split: Requirements = minimum requirements (degree, years experience). Qualifications = preferred qualifications (skills). Skills = specific technical skills. But we need to avoid duplication. Let's parse "What We Look For" bullet list: - You have a total of 2+ years of experience as a machine learning engineer or a PhD in a relevant field. - Strong Python skills and experience writing production-quality code. - Experience building and evaluating models for classification problems (preferably gradient-boosted decision trees like LightGBM/XGBoost/CatBoost, or similar). - Experience with a deep learning framework (PyTorch preferred). - Experience working with distributed data processing or parallel compute frameworks (Spark preferred; Ray/Dask 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. Then after that: "This position requires either equivalent practical experience or a Bachelor’s degree in a related field". That's a requirement. Thus we can map: - Requirements: Bachelor's degree or equivalent practical experience (maybe also 2+ years experience? but that's also a requirement). Could include both. - Qualifications: maybe the list of desired skills/experience (the bullet list). But we also have Skills heading; we could put technical skills under Skills. But we have to avoid filler. Let's decide: Section headings we will include:

About the role

-> intro paragraph.

Responsibilities

-> bullet list of What You’ll Do.

Requirements

-> bullet list: 2+ years experience as ML engineer or PhD; Bachelor's degree or equivalent practical experience.

Qualifications

-> bullet list: Strong Python skills, production-quality code; experience building/evaluating classification models (GBDT); experience with deep learning framework (PyTorch); experience with distributed data processing (Spark); experience with ML lifecycle tooling; proficient in AI-powered developer tools; ability to take problem to solution; comfortable navigating large code base, code reviews; ownership of growth, seeking feedback; strong verbal/written communication.

Skills

-> maybe we could list specific technologies: Python, LightGBM/XGBoost/CatBoost, PyTorch, Spark, Ray/Dask, Kubeflow/Airflow/MLflow, Claude Code/Cursor. But that duplicates qualifications. Better to not have Skills if it's just duplicate. But we need to decide if we have real specific content for Skills separate from Qualifications. Could treat Skills as the specific tools/languages. Qualifications as broader abilities. Let's extract: Specific tools/languages: Python, LightGBM/XGBoost/CatBoost, PyTorch, Spark, Ray/Dask, Kubeflow, Airflow, MLflow, Claude Code, Cursor. We can put those under Skills. Qualifications could be the higher-level abilities: mastering problem to solution, navigating code base, feedback, ownership growth, communication. But the bullet list includes both. We'll split. Thus: Requirements: 2+ years experience as ML engineer or PhD; Bachelor's degree or equivalent practical experience. Qualifications: Strong Python skills and production-quality code; experience building/evaluating classification models (preferably GBDT); experience with deep learning framework (PyTorch preferred); experience with distributed data processing or parallel compute frameworks (Spark preferred; Ray/Dask 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; strong verbal and written communication skills. Skills: Mastering taking a simple problem or business scenario into a solution that interacts with multiple software components, executing with clear, well-tested, extensible code; comfortable navigating a large code base, debugging others' code, providing feedback through code reviews; taking ownership of growth, proactively seeking feedback from team, manager, stakeholders. But that seems overlapping. Maybe better to keep Qualifications as the bullet list, and Skills as the specific tools (Python, LightGBM, etc). Let's do that. Thus: Qualifications bullet list: the 2+ years experience? Actually that's a requirement. So Qualifications: Strong Python skills and production-quality code; experience building/evaluating classification models (preferably GBDT); experience with deep learning framework (PyTorch preferred); experience with distributed data processing or parallel compute frameworks (Spark preferred; Ray/Dask or similar); experience with ML lifecycle tooling (Kubeflow, Airflow, MLflow); proficient in using AI-powered developer tools (Claude Code, Cursor); strong verbal and written communication skills. Skills: Mastering taking a simple problem or business scenario into a solution that interacts with multiple software components, executing with clear, well-tested, extensible code; comfortable navigating a large code base, debugging others' code, providing feedback through code reviews; taking ownership of growth, proactively seeking feedback from team, manager, stakeholders. But the "mastering..." etc are also in bullet list. Actually they are in bullet list as separate items. So we could allocate them to Skills. Thus final sections: About the role: intro paragraph. Responsibilities: bullet list of What You’ll Do. Requirements: bullet list: 2+ years experience as ML engineer or PhD; Bachelor's degree or equivalent practical experience. Qualifications: bullet list: Strong Python skills and production-quality code; experience building/evaluating classification models (preferably LightGBM/XGBoost/CatBoost); experience with deep learning framework (PyTorch preferred); experience with distributed data processing or parallel compute frameworks (Spark preferred; Ray/Dask or similar); experience with ML lifecycle tooling (Kubeflow, Airflow, MLflow or equivalent); proficient in using AI-powered developer tools (Claude Code, Cursor, or similar); strong verbal and written communication skills. Skills: bullet list: Ability to take a simple problem or business scenario into a solution that interacts with multiple software components and execute with clear, well-tested, extensible code; comfortable navigating a large code base, debugging others' code, providing feedback through code reviews; taking ownership of growth, proactively seeking feedback from team, manager, stakeholders. Pay: include pay grade, equity grade, base pay ranges, mention equity rewards, monthly stipends, benefits (but benefits also separate). We'll include Pay: Pay Grade - L, Equity Grade - 6, base pay ranges for CA/NY etc and other states, mention that base pay is part of total compensation package that may include equity rewards, monthly stipends for health, wellness and tech spending. Benefits: bullet list of benefits highlights: Health coverage at no cost (100% subsidized medical, dental, vision for employee and dependents); Monthly stipends for

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