Fabrion is an AI-native platform purpose-built for the new industrial era. Backed by 8VC.
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.
What they're looking for
- 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
More about this role
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.
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.
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...
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