Posting record
Open 12+ days
- Fabrion's site dates the posting
- Ghostless first sees this listing
- Last checked: still up
How we know
It hasn't been reposted, listed twice, or left up unusually long, and the posting doesn't say it's a talent pool.
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Last checked Nov 2, 2026.
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Skills mentioned
About the role
Founding AI Research Lead - Agentic AI Lab
San Francisco Bay Area | Full time
Backed by 8VC, we are building a world-class team to tackle one of industry's most critical problems: trusted AI for enterprise operations.
About the Role
Fabrion is designing the future of enterprise AI infrastructure, grounded in agents, knowledge graphs, and multi-tenant governance. We are working on research inside the Agentic AI Lab to train and evaluate specialized models for mission-critical enterprise work.
The direction is specific and ambitious. We share the full thesis under NDA during the interview process. What we can say here: the program has committed design partners with production data access, dedicated compute, a benchmark-first plan with clear go and no-go gates, and a platform team that has already built the governance and serving layer your models will run behind.
This is full-cycle research: problem formulation, data, training, evaluation, and deployment, with your name on the results.
Core Responsibilities
- Own the research agenda: model and training design, evaluation protocol, and the publication plan
- Take models from public benchmark results to live customer shadow deployments, with gates you define and defend
- Set the benchmark discipline: strong baselines first, published comparables cited, results that survive scrutiny
- Lead and grow a small team (ML engineer, data engineer, contractors) and pair closely with the founders and platform team
- Write technical plans internally and papers externally when results warrant it
Desired Experience
- Hands-on experience training sequence models, owning the tokenizer, the training loop, and the evaluation, not only fine-tuning through APIs
- Strong background in at least two of: reinforcement learning (especially offline and imitation settings), sequence decision modeling, structured or constrained generation, learning from event and log data
- A track record of shipping research into a product or landing a rigorous benchmark result
- PhD in machine learning or a closely related field, or an equivalent research record
- Preferred Tech Stack
- PyTorch, the Hugging Face ecosystem, experiment tracking and reproducible training pipelines, modern cloud data warehouses, evaluation harness engineering
Soft Skills & Mindset
- Comfortable as the most senior researcher in the room: setting direction under ambiguity and writing decisions down
- Rigor over hype: you distrust your own results until the baselines agree
- A teacher's instinct: part of this role is turning strong engineers into researchers
Why This Role Matters
We believe specialized models built on governed enterprise data can run real, multi-billion-dollar workflows. Your work will not be buried in research reports. It will be benchmarked in public, deployed to real customers, and activated by hundreds of thousands of decisions.
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