Senior Applied AI Engineer

• Design and implement ML-powered systems that process large-scale document data (legal, medical, billing, etc.) from raw PDFs to structured insights.
• Fine-tune LLMs and build retrieval-augmented generation (RAG) and agentic systems for real-world use cases.
• Own code from prototype to production—writing performant, maintainable Python code and integrating ML services into Supio’s core platform.
• Develop and optimize end-to-end evaluation frameworks to measure accuracy, latency, and reliability.
• Collaborate with product, design, and backend teams to bring AI features to life for real user problems.
• Work across the stack when needed: backend APIs, model serving, cloud infrastructure, and system monitoring.
• Partner directly with David Brinda (Hiring Manager) and technical leadership to shape Supio’s ML roadmap.

• Strong coding skills: You build production systems, not just research prototypes. Python expertise is required; experience with TypeScript or Go is a plus.
• Hands-on ML experience: You’ve fine-tuned models, shipped them into production, and maintained them at scale (100K+ MAU).
• Applied AI mindset: Comfortable with RAG systems, prompt engineering, agents, and data preprocessing/postprocessing for NLP tasks.
• Probabilistic thinking: You reason in uncertainty, understand performance tradeoffs, and are comfortable balancing precision and recall rather than fixed rules.
• Experience with cloud platforms (AWS, GCP, or Azure) and containerized environments for deployment.
• Clear communicator who can explain design decisions and model behavior to teammates and leaders.

• Experience in full-stack or backend engineering roles before moving into ML.
• Background in fintech, health tech, or document-heavy domains.
• Familiarity with scaling systems that process large, unstructured data (thousands of-page PDFs, mixed media, etc.).
• Grit, adaptability, and curiosity—competitive gamers or analytical thinkers who enjoy complex strategy problems tend to thrive here.

• Building ML pipelines for classification, clustering, and semantic matching.
• Deploying and monitoring fine-tuned LLMs for document summarization and case analysis.
• Implementing Supio’s homegrown RAG framework across major client streams.
• Delivering measurable impact by increasing model reliability and reducing processing time.

Compensation

• Seattle, WA: $190,000 – $255,000 annually
• San Francisco, CA: $190,000 - $255,000 annually