We are seeking an Engineering Manager to lead our ML Training & Serving team. You will directly lead a team of platform engineers and combine effective people leadership with strong technical judgment in ML infrastructure. Working closely with senior ICs and partners across Machine Learning and Infrastructure, you will help shape the team's roadmap, drive execution, and ensure the platform reliably supports Affirm's ML priorities.
What You'll Do- Partner with senior ICs and engineering leadership to define and execute the roadmap for ML training & serving, spanning model training, deployment workflows, GPU infrastructure, and low-latency model serving.
- Lead, coach, and grow a team of platform engineers while staying closely engaged in technical decisions and execution.
- Drive delivery and operational health for the team's infrastructure, balancing reliability, developer experience, performance, and cost.
- Evaluate and adopt modern ML infrastructure capabilities as Affirm's needs evolve, including transformer-based workloads and GPU compute.
- Collaborate with ML modeling, product, and infrastructure teams to ensure the platform supports Affirm's highest-priority ML initiatives.
- Recruit, develop, and retain high-performing platform engineers.
What We Look For- 7+ years of industry experience in software and/or machine learning engineering, including 2+ years managing engineers and meaningful hands-on software engineering experience.
- Strong experience building and operating production ML or distributed systems infrastructure, with hands-on experience in at least one of model training, model serving, deployment workflows, or GPU infrastructure.
- Solid understanding of ML data needs, including training datasets, data quality, reproducibility, and evaluation data.
- Familiarity with modern ML workloads, such as deep learning, transformer architectures, and large-scale training or serving.
- Strong systems thinking and the ability to partner with senior ICs on architectural trade-offs.
- Track record of delivering platforms or infrastructure that improve the productivity and impact of engineering or ML teams.
- Experience recruiting, coaching, and developing engineers across career stages.
- Effective cross-functional collaboration and the ability to lead through ambiguity.
- Experience with applied ML modeling is a plus.
- Bachelor's degree in a technical field or equivalent practical experience.
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: $181,000 - $241,000
#LI-RemoteAffirm 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
