Machine Learning Engineer - Embedded Insights

About the Role

The role entails working across the full model development lifecycle, from concept to production, and partnering with product managers, engineers, and other stakeholders to embed machine learning solutions into customer-facing products. You will be responsible for maintaining and enhancing existing machine learning systems, as well as developing new models and solutions that demonstrate customer value.

The Embedded Insights team is a key part of Plaid's mission to build a world-class suite of intelligence products, and you will have the opportunity to work with a talented team of engineers, data scientists, and product managers who are passionate about making a meaningful impact in the financial industry.

What You Will Do

• Drive machine learning initiatives from concept to production, working across the full model development lifecycle
• Leverage Plaid's unique dataset to identify high-impact opportunities for machine learning and develop proofs of concept to validate new approaches
• Build MVP solutions that demonstrate customer value and partner with cross-functional stakeholders to turn successful prototypes into scalable, customer-facing products
• Embed within product teams to translate successful prototypes into production-ready systems
• Optimize models for new use cases, improve system scalability, and incorporate customer feedback gathered before and after launch
• Maintain and enhance existing machine learning systems through feature development, retraining strategies, and robust monitoring frameworks
• Develop metrics, alerts, and dashboards that ensure model performance, reliability, and long-term health
• Collaborate with product managers, engineers, and other stakeholders to identify opportunities for machine learning and develop solutions that meet customer needs
• Stay up-to-date with industry trends and advancements in machine learning and data science, applying this knowledge to continuously improve Plaid's intelligence products

What We Are Looking For

• 5+ years of experience in machine learning, including deploying machine learning models into real-world, customer-facing systems
• High agency and creativity, with experience identifying, defining, and proposing high-impact machine learning opportunities
• Ability to analyze large and complex financial datasets to derive insights
• Advanced degree or equivalent work experience in Statistics, Economics, Mathematics, Data Science, or a related field
• Proficiency in SQL, Python, and data visualization/analysis tools
• Ability to clearly communicate complex technical systems and decision-making
• Experience working with cross-functional stakeholders, including product managers, engineers, and other teams
• Strong understanding of machine learning concepts, including supervised and unsupervised learning, deep learning, and natural language processing

Nice to Have

• Experience working with cloud-based technologies, such as AWS or Google Cloud
• Familiarity with containerization using Docker and Kubernetes
• Knowledge of agile development methodologies and version control systems, such as Git
• Experience with data engineering and architecture, including data pipelines and data warehousing

Benefits and Perks

• Competitive compensation package, including salary and equity
• Comprehensive health insurance, including medical, dental, and vision
• Generous PTO policy, including vacation, sick leave, and holidays
• Flexible working hours and remote work options
• Access to cutting-edge technologies and tools, including machine learning frameworks and data science platforms
• Opportunities for professional development and growth, including training, mentorship, and conference sponsorship
• Collaborative and dynamic work environment, with a team of talented engineers, data scientists, and product managers
• Free meals and snacks, as well as a fully stocked kitchen
• On-site fitness classes and wellness programs
• Access to exclusive company events and networking opportunities

• Tip: Make sure to highlight your experience with machine learning and data science in your resume and cover letter, as these skills are highly valued in this role.
• Be prepared to showcase your ability to communicate complex technical concepts to non-technical stakeholders, as this is a key aspect of the job.
• Consider building a portfolio of your machine learning projects, including code examples and case studies, to demonstrate your skills and experience.
• When negotiating salary, be sure to research the market rate for machine learning engineers in your area and make a strong case for your worth based on your skills and experience.
• Keep an eye out for red flags, such as unclear expectations or a lack of resources, and be prepared to ask tough questions during the interview process.
• Remember to ask about opportunities for professional development and growth, including training, mentorship, and conference sponsorship, as these can be a key factor in your long-term career success.