Machine Learning Engineer (Research Scientist) - DFAI

About the Role

The Data Foundation & AI team is a critical component of Plaid's data organization, and your work will have a direct impact on the company's mission to unlock financial freedom for everyone. You will be working with a talented team of engineers, researchers, and data scientists who are passionate about machine learning, AI, and data science.

What You Will Do

• Develop and deploy foundation models on large-scale financial datasets, leveraging techniques such as transfer learning, meta-learning, and few-shot learning
• Design, implement, and evaluate novel machine learning architectures, including but not limited to Transformers, CNNs, and RNNs
• Collaborate with product teams to identify business challenges and develop targeted solutions using machine learning and AI
• Work on pretraining objectives, fine-tuning strategies, and model evaluation frameworks to ensure robust and reliable model performance
• Develop and maintain large-scale data pipelines, leveraging tools such as Apache Beam, Apache Spark, and AWS Glue
• Implement and optimize model serving infrastructure, including but not limited to TensorFlow Serving, AWS SageMaker, and Azure Machine Learning
• Conduct thorough model analysis, including but not limited to model interpretability, explainability, and fairness
• Develop and maintain comprehensive testing frameworks, including but not limited to unit tests, integration tests, and end-to-end tests
• Present research and findings to both technical and non-technical audiences, including but not limited to internal stakeholders, customers, and external conferences
• Stay up-to-date with the latest advancements in machine learning, AI, and data science, and apply this knowledge to drive innovation and improvement in the team's work

What We Are Looking For

• MS or PhD in Machine Learning, AI, Computer Science, Statistics, or a related field
• 1-3 years of industry experience building and deploying machine learning models, with a focus on deep learning and natural language processing
• Strong applied machine learning research skills, with a focus on model development, evaluation, and deployment
• Experience with large-scale machine learning frameworks, including but not limited to TensorFlow, PyTorch, and scikit-learn
• Strong programming skills in languages such as Python, Java, or C++
• Experience with cloud-based platforms, including but not limited to AWS, Azure, or Google Cloud
• Strong understanding of data structures, algorithms, and software design patterns
• Excellent communication and collaboration skills, with the ability to work effectively with cross-functional teams
• Strong understanding of machine learning ethics, fairness, and transparency

Nice to Have

• Experience with fintech or financial data, including but not limited to banking, payments, or investments
• Experience with distributed training, including but not limited to Apache Spark, Apache Hadoop, or AWS EMR
• Experience with model explainability and interpretability, including but not limited to SHAP, LIME, or TreeExplainer
• Experience with containerization, including but not limited to Docker or Kubernetes
• Experience with agile development methodologies, including but not limited to Scrum or Kanban

Benefits and Perks

• Competitive salary and bonus structure
• Comprehensive health insurance, including medical, dental, and vision
• 401(k) matching program
• Flexible paid time off and holiday schedule
• Remote work options, including but not limited to flexible hours and work-from-home arrangements
• Professional development opportunities, including but not limited to training, conferences, and workshops
• Access to cutting-edge technology and tools, including but not limited to the latest machine learning frameworks and libraries
• Collaborative and dynamic work environment, with a focus on innovation and teamwork

• Tip: Make sure to highlight your experience with machine learning frameworks, including but not limited to TensorFlow, PyTorch, or scikit-learn.
• Tip: Emphasize your understanding of data structures, algorithms, and software design patterns, as these are critical skills for machine learning engineers.
• Tip: Be prepared to discuss your experience with cloud-based platforms, including but not limited to AWS, Azure, or Google Cloud.
• Tip: Show a strong understanding of machine learning ethics, fairness, and transparency, as these are increasingly important considerations in the field.
• Tip: Highlight any experience you have with fintech or financial data, as this is a key area of focus for Plaid.
• Tip: Be prepared to present your research and findings to both technical and non-technical audiences, as this is a critical skill for machine learning engineers at Plaid.
• Tip: Make sure to ask about the company's approach to remote work, including but not limited to flexible hours, work-from-home arrangements, and virtual collaboration tools.