Senior Software Engineer - ML Infrastructure
Plaid
Software Engineering, Other Engineering, Data Science
San Francisco, CA, USA
USD 190,800-286,800 / year
Posted 6+ months ago
We believe that the way people interact with their finances will drastically improve in the next few years. We’re dedicated to empowering this transformation by building the tools and experiences that thousands of developers use to create their own products. Plaid powers the tools millions of people rely on to live a healthier financial life. We work with thousands of companies like Venmo, SoFi, several of the Fortune 500, and many of the largest banks to make it easy for people to connect their financial accounts to the apps and services they want to use. Plaid’s network covers 12,000 financial institutions across the US, Canada, UK and Europe. Founded in 2013, the company is headquartered in San Francisco with offices in New York, Washington D.C., London and Amsterdam.
Plaid is evolving into an AI-first company, where data and machine learning are the key enablers of smarter, more secure insight products built on top of Plaid’s vast financial data network. The Machine Learning Infrastructure team sits at the center of this transformation. We build the platforms that enable model developers to experiment, train, deploy, and monitor machine learning systems reliably and at scale — from feature stores and pipelines, to deployment frameworks and inference tooling.
We are in the midst of a pivotal shift: replacing legacy systems with a modern feature store, and establishing a standardized ML Ops “golden path.” Our mission is to enable Plaid’s product teams to move faster with trustworthy insights, deploy models with confidence, and unlock the next generation of AI-powered financial experiences.
As a Senior Software Engineer on the Machine Learning Infrastructure team, you will design, build, and operate the systems that power machine learning across Plaid. You will apply your deep technical expertise to create scalable, reliable, and secure ML platforms, and collaborate closely with ML product teams to accelerate the delivery of ML & AI-powered products.
This is a highly technical, hands-on role where you’ll contribute to core infrastructure, influence architectural direction, and mentor peers while helping to define the “golden path” for ML development and deployment at Plaid.
Responsibilities
- Design and implement large-scale ML infrastructure, including feature stores, pipelines, deployment tooling, and inference systems.
- Drive the rollout of Plaid’s next-generation feature store to improve reliability and velocity of model development.
- Help define and evangelize an ML Ops “golden path” for secure, scalable model training, deployment, and monitoring.
- Ensure operational excellence of ML pipelines and services, including reliability, scalability, performance, and cost efficiency.
- Collaborate with ML product teams to understand requirements and deliver solutions that accelerate experimentation and iteration.
- Contribute to technical strategy and architecture discussions within the team.
- Mentor and support other engineers through code reviews, design discussions, and technical guidance.
Qualifications
- 5+ years of industry experience as a software engineer, with strong focus on ML/AI infrastructure or large-scale distributed systems.
- Hands-on expertise in building and operating ML platforms (e.g., feature stores, data pipelines, training/inference frameworks).
- Proven experience delivering reliable and scalable infrastructure in production.
- Solid understanding of ML Ops concepts and tooling, as well as best practices for observability, security, and reliability.
- Strong communication skills and ability to collaborate across teams.
- [Nice to have] Experience with ML Ops tools such as MLFlow, SageMaker, or model registries.
- [Nice to have] Exposure to modern AI infrastructure environments (LLMs, real-time inference, agentic models).
- [Nice to have] Background in scaling ML infrastructure in fast-paced product environments.
Our mission at Plaid is to unlock financial freedom for everyone. To support that mission, we seek to build a diverse team of driven individuals who care deeply about making the financial ecosystem more equitable. We recognize that strong qualifications can come from both prior work experiences and lived experiences. We encourage you to apply to a role even if your experience doesn't fully match the job description. We are always looking for team members that will bring something unique to Plaid!
Plaid is proud to be an equal opportunity employer and values diversity at our company. We do not discriminate based on race, color, national origin, ethnicity, religion or religious belief, sex (including pregnancy, childbirth, or related medical conditions), sexual orientation, gender, gender identity, gender expression, transgender status, sexual stereotypes, age, military or veteran status, disability, or other applicable legally protected characteristics. We also consider qualified applicants with criminal histories, consistent with applicable federal, state, and local laws. Plaid is committed to providing reasonable accommodations for candidates with disabilities in our recruiting process. If you need any assistance with your application or interviews due to a disability, please let us know at [email protected].
190800 - 286800 USD a year
The target base salary for this position ranges from $190,800/year to $286,800/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.