Founding AI Research Lead - Agentic AI Lab

Fabrion • San Francisco, CA

1d • Onsite

About The Position

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.

Requirements

• 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

Nice To Haves

• PyTorch, the Hugging Face ecosystem, experiment tracking and reproducible training pipelines, modern cloud data warehouses, evaluation harness engineering

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

What This Job Offers

• Job Type: Full-time
• Career Level: Senior
• Education Level: Ph.D. or professional degree