Founding AI Lead
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.
Apply Now
Apply Now
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.
Apply Now
Apply Now