Background
Verification-centered research engineer: AI accelerates the experimentation, controlled evaluation decides what survives, and what survives ships at scale.
Research: multi-agent coordination, continual learning
Production: ML systems, cloud infrastructure
Staff Engineer at GSV AI Labs, the AI division of the private equity firm Greater Sum Ventures, where we build CharlieIQ — an AI operational suite for vertical SaaS companies that hands engineering, marketing, sales, support, and planning work to agents, with a human approving every consequential action. I was the lab's third engineering hire and its first Staff Engineer.
That work spans Minions, CharlieIQ's multi-agent engineering team; TenantText, its AI resident-communication platform for property managers; and Virtual Front Desk.
Separately: AI initiatives across Greater Sum Ventures' portfolio companies, including cross-portfolio fraud detection.
Before that, Co-Technical Lead in HCA Healthcare's Accelerated Technologies group, shipping production clinical AI (document intelligence, clinical RAG) at the largest US hospital system; and an Advanced RPA Developer in Accenture's Automation Center of Excellence.
Scope & Impact
Cross-org influence
AI initiatives spanning Greater Sum Ventures' portfolio companies, including cross-portfolio fraud detection. Separately: built an internal user-feedback platform at HCA Healthcare — routing input from users to development and product teams — pulled in across the advanced-technology division by teams that didn't report to the same org.
Systems at scale
Shipped document-intelligence and clinical RAG pipelines in production at HCA Healthcare, the largest US hospital system.
Technical direction
Staff-level platform and architecture decisions on CharlieIQ's agent systems. Aegis (event-sourced state, policy gateway, verifiable commitments) is the same kind of infrastructure judgment applied to agent durability.
Ambiguity
Healthcare's regulatory and organizational environment doesn't hand you a spec: HIPAA constraints, clinical stakeholders, and HCA Healthcare's scale as an operating environment all had to be reconciled into one architecture, not designed around in isolation.
Research
Multi-agent coordination
Grounded Commitment Learning: verifiable behavioral contracts for AI coordination. Applies Hart-Moore incomplete contract theory. 40.4% hold-up reduction, re-derived by CI on every push.
Continual learning
Collaborative Nested Learning: multi-timescale optimization with non-adjacent knowledge bridges. +89% accuracy at high regularization. Pareto-dominant across retention-accuracy tradeoff.
Methods
Claims move through a five-step progression — empirical research, mechanistic validation, formal foundations, executable research, production systems — where each step is what the previous one has to survive before a result is accepted. AI participates freely in the first step and is trusted in none of the rest. The full version, with what currently backs each step, is on the homepage.
Production Experience
Document understanding pipeline
Multi-provider LLM routing with confidence-based escalation. HIPAA-compliant. Human-in-the-loop for low-confidence outputs; corrections captured as reward-shaped labels for offline fine-tuning.
Mellifera — voice-first field capture
Hands-free inspection logging for beekeepers working in gloves, with LLM-powered NLU turning speech into structured records. The decision-support models it feeds are specified but not yet trained.
Technical Stack
Languages: Python, TypeScript, SQL
ML: PyTorch, TensorFlow, TensorFlow.js
Infrastructure: GCP, Terraform, Docker, CI/CD
Data: PostgreSQL, BigQuery, vector databases
Deployment: Edge, cloud, hybrid
Education
The path here runs through language, not code: six years in Paris working in a second language led to coaching French executives on American business communication and founding a tutoring company, which led to automation — RPA development at Accenture's Automation Center of Excellence (among the first US-based Advanced RPA Developers), alongside software engineering at Atiba Software — and eventually to healthcare-scale ML.
M.A. Université de Paris VII — Denis Diderot (French-language program)
Littérature, Langues, et Civilisations des Pays Anglophones
Research Interests
Pre-representational computation—the operations that exist before and enable representation (whether human language, embeddings, or any other representational layer).
- Which compositions of projection, attention, and regularization preserve structure vs. destroy it?
- How do systems learn priors over which operations to apply when?
- Verifiable behavior grounded in observable actions (scalable oversight)
- Alignment for systems whose operations are themselves learned
Contact
jason@jasonstiltner.com · GitHub · LinkedIn · Resume available on request