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Staff Machine Learning Engineer (ML Portfolio)

Staff Machine Learning Engineer (ML Portfolio)

AffirmSan Jose, CA, United States
30+ days ago
Job type
  • Full-time
Job description

Join to apply for the Staff Machine Learning Engineer (ML Portfolio) role at Affirm

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Join to apply for the Staff Machine Learning Engineer (ML Portfolio) role at Affirm

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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.

The Portfolio ML team manages and optimizes the flow of loan opportunities across Affirm-owned and operated properties. We build models, tools, and libraries to support portfolio management actions that balance unit economics, product growth, and user experience. We power ROA-based decisioning; real-time and offline conversion rate prediction; counterfactual portfolio simulation; and the valuation of loans. We sit within the Decisions Foundations org.

What You’ll Do

  • You will be responsible for setting technical strategy for your team on a year-long time scale, and help your team tie it together with critical, business-impacting projects.
  • You will collaborate across teams in the product development lifecycle by collaborating with product management, design & analytics to ensure technical sustainability, risks and trade-offs are well understood and managed.
  • You will act as a force-multiplier for your team through your definition and advocacy of technical solutions and operational processes.
  • You take ownership of your team’s operations and availability by ensuring you have the right monitoring, triage rotations, playbooks, polcities, testing and alerting in place to support “keep the lights on” & on-call efforts.
  • You will foster a culture of quality and ownership on your team by setting code review and design standards for your team, and advocating for them beyond your team through your writing and tech talks.
  • You will help develop talent on your team by providing feedback and guidance, and leading by example.

What We Look For

  • Bachelors in a technical field with 8+ years of industry experience. Relevant PhD can count for up to 2 YOE.
  • Proficiency in machine learning with experience in areas such as Generalized Linear Models, Gradient Boosting, Deep Learning, and Probabilistic Calibration. Domain knowledge in credit risk, portfolio management, learning to rank, and personalization is a plus
  • Strong engineering skills in Python and data manipulation skills like SQL
  • You have experience delivering major features, system components or deprecating existing functionality in a system through the definition of a technical and execution plan. You write high quality code that is easily understood and used by others.
  • You thrive in ambiguity, and are comfortable moving from low level language idioms all the way to the architecture of large systems to understand how they work.
  • Your growth and impact trajectory demonstrates that you have mastered gathering and iterating on feedback from your engineering and cross-functional peers.
  • 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.

    • Base Pay Grade - P

    Equity Grade - 13

    USA base pay range (CA, WA, NY, NJ, CT) per year : $225,000 - $275,000

    USA base pay range (all other U.S. states) per year : $200,000 - $250,000

    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.)

    Location : Remote - US

    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.

    Benefits

    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.

    [For U.S. positions that could be performed in Los Angeles or San Francisco] 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.

    Seniority level

    Seniority level

    Mid-Senior level

    Employment type

    Employment type

    Full-time

    Job function

    Job function

    Engineering and Information Technology

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    Staff Machine Learning Engineer • San Jose, CA, United States