Staff Machine Learning Scientist

Employment Type: Full-time Location Type: Remote (United States) or Oakland, CA

Position Overview

As a Staff Machine Learning Scientist within our Data+ML organization, you will lead the development of customer-facing, production-deployed Machine Learning capabilities to address domain problems within the payments space. This role requires a strong understanding of ML development methodology and theory, as well as modern MLOps practices and techniques. You will also contribute to the technical trajectory of our ML ecosystem, support our AI strategy in alignment with business objectives, and drive excellence in our ML development practices.

This role reports directly to the Sr. Director of Machine Learning and will be instrumental in defining and delivering foundational ML capabilities to support Marqeta’s business and AI strategy.

Marqeta operates on a Flexible First model, allowing this role to be performed remotely within the United States or from our Oakland, CA headquarters.

The Impact You'll Have

• Develop ML models and supporting technical capabilities across the end-to-end ML development lifecycle, from ideation and prototyping through production deployment and monitoring.
• Build ML capabilities that address critical internal and external customer-facing challenges in the payments space, including risk, fraud, disputes processing, and named entity recognition.
• Collaborate with key Go-To-Market, Product, and Engineering stakeholders to identify model development opportunities, guide decision-making by leveraging payments expertise, and create prototypes to assess feasibility and expected business value.
• Partner with legal, regulatory, and risk stakeholders to ensure compliance of ML development efforts with all applicable legal and regulatory requirements.
• Work with Product and Technical Program Managers to align the technical vision for our ML ecosystem with the needs of our ML scientists.

Requirements

• 6+ years of experience in a data science, machine learning engineering, or similar role within the financial services, banking, or payments space.
• 6+ years of expertise in building and operating large-scale, customer-facing, production ML capabilities.
• Strong understanding of ML techniques, including supervised, unsupervised, and reinforcement learning.
• Strong understanding of MLOps and the ML development lifecycle, particularly using AWS SageMaker and related services.
• Excellent communication and interpersonal skills, with the ability to cultivate relationships across teams, influence decision-making, and collaborate with stakeholders at all levels of the organization.
• A strong bias toward action and the ability to operate proactively and effectively in a dynamic, fast-paced environment.
• High ethical standards and a commitment to promoting a strong culture.
• BS degree preferred.

Your Manager

Marek Cyran

Recruiter For This Role

Kayla Osuna

Compensation and Benefits

Marqeta is a Flex First company, offering you the flexibility to choose your working environment, whether that be from home or at a company office. To support this, we calibrate pay to a competitive value according to your working location within the United States. Compensation is aligned according to three tiers:

• National: A baseline tier applicable to most geographic territories in the United States.
• Premium: Slightly elevated from the National tier, oriented toward a narrower set of higher cost-of-living areas (e.g., Los Angeles, CA; Seattle, WA).
• Premium Plus: A tier for the most expensive working areas (e.g., San Francisco Bay Area; New York City).

Please visit the provided page or consult with a Recruiter to determine which tier is applicable to you.

When determining salaries, we consider several factors, including but not limited to skills, prior experience, and work location. The new-hire base salary range for this position is:

• National: $176,800 - $221,000
• Premium: $191,400 - $239,200
• Premium Plus: $208,000 - $260,000

We also believe in recognizing talent and contribution through additional benefits.