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Staff Machine Learning Engineer

extra holidays - extra parental leave - work from home - fully flexible

Remote:

Full Remote

Contract:

Full time

Salary:

178 - 228K yearly

Experience:

Senior (5-10 years)

Work from:

Canada

Offer summary

Qualifications:

8+ years of experience in machine learning engineering, Relevant PhD can count for up to 2 years, Proficiency in ML models and data manipulation skills, Experience with large scale distributed systems like Spark or Ray, Familiarity with open source ML projects and tools.

Key responsabilities:

Develop machine learning models predicting default likelihood
Collaborate with engineering teams for model training and monitoring
Research and prototype solutions for credit decisioning
Scale data pipelines, features, and algorithms
Define requirements for new products with cross-functional teams

Affirm Financial Services Large https://www.affirm.com/

1001 - 5000 Employees

See more Affirm offers

Job description

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.

Join the Affirm team as a Staff Machine Learning Engineer and contribute to the success of our ML Underwriting team. We are the driving force behind Affirm's core value proposition, leveraging cutting-edge machine learning to assess creditworthiness throughout the life cycle of loan applications.

As a Staff Machine Learning Engineer on our team, you will be at the forefront of developing high-quality, production-ready models that play a central role in our decision-making processes. Your contributions will be instrumental in shaping our financial landscape. If you have a strong interest in machine learning and enjoy challenging work, Affirm is the place for you!

What You'll Do

Use Affirm’s proprietary and other third party data to develop machine learning models that predict the likelihood of default and make an approval or decline decision to achieve business objectives
Partner with platform and product engineering teams to build model training, decisioning, and monitoring systems
Research ground breaking solutions and develop prototypes that drive the future of credit decisioning at Affirm
Implement and scale data pipelines, new features, and algorithms that are essential to our production models
Collaborate with the engineering, credit, and product teams to define requirements for new products

What We Look For

8+ years of experience as a machine learning engineer. Relevant PhD can count for up to 2 YOE
Experience developing machine learning models at scale from inception to business impact
Proficiency in machine learning with experience in areas such as Generalized Linear Models, Gradient Boosting, Deep Learning, and Probabilistic Calibration
Strong engineering skills in Python and data manipulation skills like SQL
Experience using large scale distributed systems like Spark or Ray
Experience using open source projects and software, such as scikit-learn, pandas, NumPy, XGBoost, PyTorch, Kubeflow
Experience with Kubernetes, Docker, and Airflow is a plus
Excellent written and oral communication skills and the capability to drive cross-functional requirements with product and engineering teams
Persistence, patience and a strong sense of responsibility – we build the decision making that enables consumers and partners to place their trust in Affirm

Base Pay Grade - P

Equity Grade - 7

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 monthly stipends for health, wellness and tech spending, and benefits (including 100% subsidized medical coverage, dental and vision for you and your dependents). In addition, the employees may be eligible for equity rewards offered by Affirm Holdings, Inc. (parent company).

CAN base pay range per year: $178,000 - $228,000

#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.

[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 the Affirm Employment Privacy Policy for applicants within the United States, the EU Employee Notice Regarding Use of Personal Data (Poland) for applicants applying from Poland, the EU Employee Notice Regarding Use of Personal Data (Spain) for applicants applying from Spain, or the Affirm U.K. Limited Employee Notice Regarding Use of Personal Data for applicants applying from the United Kingdom, and hereby freely and unambiguously give informed consent to the collection, processing, use, and storage of your personal information as described therein.