Staff Machine Learning Engineer (Research Scientist) - DFAI

About the Role

As a key member of the team, the Staff Machine Learning Engineer will drive decisions from pretraining objectives to production deployment, focusing on foundation models that can be leveraged by various teams across the company. This includes overseeing the full machine learning lifecycle, from data curation and experimentation to production serving and evaluation. The goal is to establish rigorous evaluation frameworks and build scalable pipelines that translate research into production impact.

The Staff Machine Learning Engineer will also collaborate closely with cross-functional teams to define how products integrate with and adapt foundation models, thereby enabling reusable ML infrastructure and reducing duplicated modeling efforts. Given the seniority of the role, mentoring engineers and elevating engineering standards will be an essential part of the job, along with communicating technical advancements both internally and externally as a representative of Plaid's AI and machine learning capabilities.

What You Will Do

• Own the end-to-end technical strategy for a foundation model, from pretraining architecture to production serving, ensuring it aligns with Plaid's product vision and technical goals.
• Conduct research that has direct application and impact, driving technical decisions from experimentation through production systems that serve real customers and power multiple product teams.
• Work across the full ML stack, including pretraining objectives, architecture design, distributed training, serving infrastructure, monitoring, and cross-team integration, to ensure seamless execution of foundation models.
• Set technical direction and mentor a high-caliber team of engineers, amplifying their capabilities and the impact of product teams across Plaid through technical leadership and guidance.
• Help achieve greater financial freedom for hundreds of millions of consumers by building and shipping ML capabilities that integrate with Plaid's financial network data.
• Collaborate with product teams to integrate foundation models into their applications, ensuring seamless adaptation and utilization of these models.
• Establish and maintain scalable, repeatable pipelines for translating research into production-ready models, focusing on efficiency, reliability, and performance.
• Develop and implement rigorous evaluation frameworks to measure model performance across diverse use cases, ensuring that foundation models meet the high standards of Plaid's product suite.
• Work closely with the data curation team to ensure high-quality data sets for model training, focusing on data integrity, relevance, and coverage.
• Participate in the design and development of new features and models, providing technical expertise and guidance to cross-functional teams.

What We Are Looking For

• MS or PhD in Computer Science, Machine Learning, or related fields, with 7-12+ years of industry experience for MS holders or 5-9+ years for PhD holders, demonstrating a strong track record of technical leadership and production delivery.
• Deep expertise in Transformers, LLMs, and foundation models, including pretraining objectives, architecture design, and fine-tuning approaches.
• Prior technical leadership experience, with evidence of cross-team influence, mentorship, and the ability to drive technical strategy and direction.
• Strong background in machine learning infrastructure, applied AI, and distributed systems, with experience in working on large-scale data curation and model pretraining.
• Experience with production deployment, feature management, and observability, ensuring that models are not only developed but also successfully integrated into production environments.
• Excellent communication skills, with the ability to articulate complex technical concepts to both technical and non-technical audiences, internally and externally.
• Ability to work in a fast-paced, dynamic environment, with a focus on innovation, experimentation, and continuous learning.
• Commitment to remote work principles, with flexibility and adaptability in working with distributed teams across different time zones.

Nice to Have

• Experience with cloud-based infrastructure, such as AWS or GCP, and containerization using Docker.
• Knowledge of agile development methodologies and version control systems like Git.
• Familiarity with data visualization tools and techniques, for presenting complex data insights to stakeholders.
• Participation in open-source projects or personal projects related to machine learning and AI, demonstrating a passion for innovation and community engagement.
• Certification in machine learning or related fields, such as Certified Data Scientist or Certified Machine Learning Engineer.

Benefits and Perks

• Competitive salary and equity package, reflecting the seniority and impact of the role.
• Comprehensive health, dental, and vision insurance, ensuring the well-being of employees and their families.
• Flexible PTO policy, allowing for a healthy work-life balance and the freedom to take time off as needed.
• Remote work stipend, providing support for setting up a productive home office or coworking space.
• Professional development opportunities, including training, conferences, and workshops, to foster continuous learning and growth.
• Access to cutting-edge technologies and tools, enabling engineers to work with the latest advancements in machine learning and AI.
• Collaborative and dynamic work environment, with a team of talented and motivated professionals passionate about fintech and innovation.
• Opportunity to work on high-impact projects, with the potential to make a significant difference in the financial lives of millions of people.

• Build a strong portfolio: Showcase your experience with machine learning models, especially foundation models, and highlight any open-source contributions or personal projects that demonstrate your expertise.
• Emphasize leadership experience: Given the seniority of the role, it's crucial to highlight any previous technical leadership positions, including mentorship, team management, and cross-functional collaboration.
• Prepare for technical interviews: Review common machine learning interview questions, practice coding challenges, and be ready to discuss your experience with ML infrastructure, applied AI, and distributed systems.
• Research Plaid's technology stack: Familiarize yourself with Plaid's current projects, technological advancements, and challenges to demonstrate your interest and potential fit for the role.
• Highlight your ability to work remotely: As the role is remote, emphasize your experience with remote collaboration tools, your ability to manage your time effectively in a distributed team, and your strategies for staying connected with colleagues across different time zones.
• Discuss your approach to continuous learning: Given the fast-paced nature of the fintech and AI industries, highlight your commitment to ongoing education, including courses, workshops, or certifications that you've pursued to stay up-to-date with the latest developments.