Skills this job asks for
Machine learningProduct managementA/B testingPythonSpark
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Sponsorship verdict
Sponsorship possible
One solid signal, not two. Worth applying, and worth asking about sponsorship early.
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✓
Employer is on a government sponsor recordThe US Department of Labor certified 27 H-1B/E-3 labor condition applications for this employer between Oct 2025 and Jun 2026 (latest Jun 2026) — the step every H-1B hire needs first.
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The posting doesn’t mention sponsorshipSilence isn’t a refusal — ask the recruiter before investing much time.
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No salary bar for this routeH-1B has no fixed salary bar: the employer must pay at least the prevailing wage for the role and area.
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✓
What Plaid INC paid sponsored hires in similar roles2 certified filings for “Senior Machine Learning Engineer” (Data Scientists) in NY: $170k–$226k, median $198k.
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Confirmed live todayWhen a source last listed this job as open.
Details and sources
- Employer is on a government sponsor record
- The US Department of Labor certified 27 H-1B/E-3 labor condition applications for this employer between Oct 2025 and Jun 2026 (latest Jun 2026) — the step every H-1B hire needs first. Source: LCA disclosure data (US Department of Labor (OFLC)).
- No salary bar for this route
- H-1B has no fixed salary bar: the employer must pay at least the prevailing wage for the role and area. Cap-subject employers enter a lottery weighted by wage level. Source: https://www.federalregister.gov/documents/2025/12/29/2025-23853/weighted-selection-process-for-registrants-and-petitioners-seeking-to-file-cap-subject-h-1b, rules effective 2026-02-27.
- What Plaid INC paid sponsored hires in similar roles
- 2 certified filings for “Senior Machine Learning Engineer” (Data Scientists) in NY: $170k–$226k, median $198k. Most were filed at wage level IV (100%) — 4 lottery entries, ≈61% projected selection odds for cap-subject employers. Source: US Department of Labor LCA disclosure data (Oct 2025 – Jun 2026).
US H-1B: cap-subject employers enter a lottery weighted by wage level — Level I gets 1 entry, Level IV gets 4 (DHS projected selection odds ≈15% at Level I to ≈61% at Level IV). Universities and non-profit research employers are cap-exempt. The $100,000 fee for new petitions from abroad is currently blocked by a court order (appeal pending).
A verdict summarises public evidence. It is not legal advice and never a guarantee: the employer and the immigration authority decide. Sign in to factor in where you can already work.
About the role
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, Seattle, Washington D.C., Raleigh, London, and Amsterdam.
ABOUT THE ROLE
As the Manager of Machine Learning Engineering on the Credit team, you’ll lead the team responsible for Plaid’s LendScore and Income Insights products. You’ll manage and develop a team of Machine Learning Engineers and Data Scientists, set direction for advancing our insights, and partner with Engineering, Product, and other Data teams to deliver customer and business impact.
In this role, you will:
- Lead, support, and develop a team of Machine Learning Engineers and Data Scientists working across credit insights products.
- Set technical and product direction for evolving LendScore and Income Insights, including the team’s machine learning roadmap.
- Partner with Engineering, Product, and Data teams to prioritize opportunities and deliver reliable, scalable solutions.
- Guide the development of machine learning models across the full lifecycle, from experimentation and offline training to online serving and monitoring.
- Help the team tackle both 0-to-1 opportunities and the scaling and improvement of established products.
- Foster a high-ownership, bottom-up team culture grounded in data-driven decisions and business impact.
WHAT EXCITES YOU
- Leading work that can expand access to financial services for millions of people through well-known and emerging fintech applications.
- Developing products and insights that support the next wave of cash-flow-based underwriting.
- Helping a talented team experiment with modern machine learning techniques and bring them into production.
- Shaping a roadmap that spans new 0-to-1 opportunities and scaling solutions that are already delivering value.
- Working across Data, Engineering, and Product in a high-ownership environment where the team’s work can drive meaningful impact.
WHAT EXCITES US
- 8+ years of experience training and serving AI or machine learning models in production environments.
- Experience leading a team of Data Scientists and Machine Learning Engineers.
- Experience in fintech lending and a strong understanding of how models are built and used in lending.
- Experience building or working with data-intensive backend applications in large, distributed systems.
- Ability to code and iterate independently using data infrastructure tools such as Python, Spark, Jupyter notebooks, and standard machine learning libraries.
- A track record of taking ownership and driving projects to business impact.
- Ability to work effectively with both technical and non-technical teams.
- Master’s degree or equivalent work experience in Computer Science, Mathematics, Engineering, or a closely related field.
- Nice to have: Experience in data analytics or data engineering.
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 pri
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