Engineering Manager - Machine Learning Infrastructure
Plaid
Responsibilities
- Lead and support the ML Infra team, driving project execution and ensuring delivery on key commitments.
- Build and launch Plaid’s next-generation feature store to improve reliability and velocity of model development.
- Define and drive adoption of an ML Ops “golden path” for secure, scalable model training, deployment, and monitoring.
- Ensure operational excellence of ML pipelines, deployment tooling, and inference systems.
- Partner with ML product teams to understand requirements and deliver solutions that accelerate model development and iteration.
- Recruit, mentor, and develop engineers, fostering a collaborative and high-performing team culture.
Qualifications
- 8–10 years of experience in ML infrastructure, including direct hands-on expertise as an engineer, IC/TL.
- 2+ years of experience managing infrastructure or ML platform engineers.
- Proven experience delivering and operating ML or AI infrastructure at scale.
- Solid technical depth across ML/AI infrastructure domains (e.g., feature stores, pipelines, deployment, inference, observability).
- Demonstrated ability to drive execution on complex technical projects with cross-team stakeholders.
- Strong communication and stakeholder management skills.
216000 - 367200 USD a year
The target base salary for this position ranges from $216,000/year to $376,200/year [in Zone 1, in Zone 4 or encompassing all Zones]. The target base salary will vary based on the job's location.
Our geographic zones are as follows:
Zone 1 - San Francisco / New York City / Seattle
Zone 2 - Los Angeles / Washington DC / Austin / Boston / Sacramento / San Diego
Zone 3 - Atlanta / Portland / Chicago / Philadelphia / Denver / Miami / Dallas / Raleigh
Zone 4 - All other US cities
The base salary range listed for this full-time position excludes commission (if applicable), equity and benefits. The pay range shown on each job posting is the minimum and maximum target for new-hire salaries. Actual pay may be higher or lower depending on factors like skills, experience, and relevant education or training.