Required Staff Software Engineer, AI
Company Overview
We are the global financial technology platform that powers prosperity for the people and communities we serve. With approximately 100 million customers worldwide using products such as TurboTax, Credit Karma, QuickBooks, and Mailchimp, we believe that everyone should have the opportunity to prosper. We never stop working to find new, innovative ways to make that possible.
Job Overview
Come join the team as a Staff AI Engineer.
Our Data Exchange group is responsible for acquiring millions of transactions and statements a day to satisfy our customers needs in all our products.
You will utilize your skills to help develop and maintain backend services leveraging AI and machine learning models, using both analytical algorithms and deep learning approaches, to acquire data from financial institutions on behalf of our users.
Responsibilities:
Lead and apply best practices in AI driven software lifecycle management, from ideation, through development and evaluation to production deployment.
Build a backend service with AI in its core at scale (millions of users and requests daily)
Collaborate with stakeholders to define success criteria and align model metrics with business goals
Work side-by-side with product managers, business analytics, data scientists, and backend engineers in enabling AI solutions for business use cases
Explore the state-of-the-art technologies and apply them to deliver customer benefits
Requirements: 8+ years industry experience
2+ years industry experience bringing AI models from modeling to production
Expertise and experience in data mining algorithms and statistical modeling techniques such as classification, regression, clustering, anomaly detection, and text mining
Strong understanding of the Software design and architecture process
Experienced with working in cloud production-grade high-scale microservices environment
Languages such as Python & Java
Building and maintaining AI based applications at scale in production
Experience with agentic systems or multi-agent orchestration in AI workflows and AI observability practices.
Exposure to Knowledge Graphs, RAG (Retrieval-Augmented Generation), or semantic search.
Understanding of AI infrastructure components, including the prompt lifecycle, fallback logic, and feature-level configuration.
Excellent oral and written English communication skills: demonstrated ability to explain complex technical issues to both technical and non-technical audiences
BS, MS in an appropriate technology field (Computer Science, Statistics, Applied Math, Operations Research), or equivalent work experience
Advantage:
Data science model training:
Well versed in Data Science languages, tools and frameworks, including data processing platforms and distributed computing systems (for example Python, R, SQL, SKLearn, NumPy, Pandas, TensorFlow, Keras)
Familiarity with vector database
Machine Learning experience.
This position is open to all candidates.