Member of the Technical Staff — AI/ML

Stuut•San Francisco, CA

About The Position

Stuut is transforming accounts receivable for B2B companies—making collections smarter and faster for companies that have historically relied on manual processes that are labor intensive and costly. Our platform is gaining traction with finance teams across industrials, chemicals, and manufacturing sectors from Fortune 10 brands to scaling midmarkets. We're backed by top-tier investors including a16z, Khosla, Activant, 1984 Ventures and Page One. We’re hiring a Member of Technical Staff – AI/ML to design, build, and deploy AI-powered systems that solve real-world financial operations challenges. You’ll take state-of-the-art AI research and translate it into production-grade features that deliver measurable customer impact. From intelligent invoice matching to automated payment reconciliation, you’ll create scalable, reliable AI applications that integrate seamlessly into our platform. This is a hands-on role for an engineer who thrives at the intersection of AI innovation and practical business application — turning cutting-edge models into real-world value for midmarket CFOs.

Requirements

5+ years of software engineering experience, with at least 2 years focused on applied AI/ML in production environments.
Integrated and fine-tuned LLMs for real-world applications.
Built retrieval-augmented generation (RAG) systems for document-intensive workflows.
Understand MLOps best practices, from model deployment pipelines to monitoring and A/B testing.
Fluent in Python and experienced with PyTorch, TensorFlow, and Transformers.
Shipped AI products that solved real business problems — not just prototypes.
Strong experience with data preprocessing and feature engineering for AI.
Can translate business requirements into clear, impactful technical solutions.
Experience integrating AI systems into existing enterprise platforms.

Responsibilities

Build & Deploy: Create production-ready AI applications that directly address customer financial workflow challenges.
Optimize AI Features: Implement and refine capabilities like invoice matching, payment reconciliation, and financial document processing.
Adapt & Fine-Tune Models: Apply and optimize LLMs, RAG systems, and other AI architectures for specific financial use cases.
Engineer for Scale: Develop robust AI pipelines from data ingestion through inference, ensuring reliability, scalability, and maintainability.
Automate Workflows: Design AI-powered automations that reduce manual effort in finance operations.
Collaborate Cross-Functionally: Partner with Product, Engineering, and customers to translate business needs into effective AI solutions.
Measure Impact: Create evaluation frameworks to track AI system performance and quantify business outcomes.