Founding AI Research Lead - Agentic AI Lab
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Before you apply
SalaryNo pay range posted
LocationSan Francisco Bay Area
Work modeon-site
Typefull-time
SeniorityLead
DepartmentEngineering
Company size11–50 people
PostedSep 6, 2026 · 2mo ago
Verified live22h ago
May no longer be actively hiring
90% of this company's open roles have been listed for 90+ daysNo pay range listed, though California requires one
At a glanceSummarised by Seekless from the posting.
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
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
Skills
PyTorch
Hugging Face
cloud data warehouses
Founding AI Research Lead - Agentic AI Lab
San Francisco Bay Area | Full time
About the role
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
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Own the research agenda: model and training design, evaluation protocol, and the publication plan
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Take models from public benchmark results to live customer shadow deployments, with gates you define and defend
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Set the benchmark discipline: strong baselines first, published comparables cited, results that survive scrutiny
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Lead and grow a small team (ML engineer, data engineer, contractors) and pair closely with the founders and platform team
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Write technical plans internally and papers externally when results warrant it
Desired Experience
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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.