Edisonian

Compensation

Full Description

Founding AI (CTO or Founding AI Lead) — Edisonian About Edisonian We’re building an AI + materials platform that fuses advanced ML with structured experimental data to radically speed up the discovery and optimization of new materials. Role Overview As CTO & Co-Founder (or Founding AI Lead), you will define and execute the technical vision. You’ll be both architect and hands-on builder early, then scale the team as we grow. ResponsibilitiesAI Platform Development • Design and build LLM+RAG systems over patents/literature and structured lab data; stand up clean APIs/services. • Own the experimental data model (units, ontology, lineage, uncertainty) and guardrails for reproducibility. • Stand up evaluation harnesses for RAG (retrieval precision/recall, groundedness) and agents (tool success, latency, cost). • Integrate frontier and open models; fine-tune on proprietary corpora; enforce data contracts and provenance. ML for Materials Optimization • Implement uncertainty-aware BO (GP/BNN/ensembles) and hybrid DL to guide high-throughput experiments. • Build closed-loop/active-learning pipelines; support multi-objective trade-offs (performance, stability, cost). • Add simulation-in-the-loop when experiments are scarce; design batch policies and robust evals (regret, sample efficiency). Technical Leadership • Own the technical roadmap; make pragmatic build/buy/open decisions. • Hire/mentor early Eng/ML ICs; set quality, security, and reliability bars. • Define protocols/APIs to lab automation (scheduler, run registry, result ingestion) and enforce provenance tracking end-to-end. Co-Founder • Partner on vision, fundraising, and strategic collaborations; represent tech to investors, customers, and labs. What you’ll ship (90–180 days) • v0: RAG over our corpus + experiment archive; reproducible pipelines; baseline eval dashboard. • v1: Production data schema + lineage service; closed-loop optimizer that learns from lab feedback and delivers ≥ X% improvement on a pilot materials target. • Interfaces to HTE/robotics or contract labs (mocked if hardware isn’t ready). Qualifications • PhD/MS in CS/ML/Comp Physics/Materials (or equivalent experience). • Deep applied ML: LLMs, RAG, Bayesian optimization, neural nets; shipped systems in startup or research-to-product settings. • Strong with scientific/experimental data; Python + PyTorch/TensorFlow + Hugging Face; from notebook → service. • Bias to action in ambiguous, high-velocity environments. Nice to Have • Lab automation/robotics/high-throughput experimentation. • Publications/patents/OSS in AI/ML or materials informatics. • Prior team leadership or early-stage founder/IC experience. Our Stack (indicative) Python, PyTorch, HF, vector DB + retrieval tooling, LangChain/LlamaIndex, containerized services on cloud; data contracts/metadata schema + lineage; MLOps (tracking, evals, CI/CD). Compensation & Location • Founding equity: meaningful stake. • Cash: dependent on funding; competitive for early stage; flexible for exceptional fit. • Location: Bay Area preferred. Remote-friendly (US). • Visa: case-by-case. • EOE. If you’re excited but don’t tick every box, we still want to hear from you.