Trajectory raises $45M from Sequoia to automate model tuning that companies now hand to FDEs
Trajectory, an 18-person continual-learning startup, raised a $45M Series A at a $300M valuation led by Sequoia. It pitches its post-training platform as a cheaper alternative to hiring research teams or forward-deployed engineers to fine-tune models for specialised work.
Trajectory, founded about a year ago by three Stanford classmates with backgrounds at Google DeepMind and Apple, has raised a $45 million Series A at a $300 million valuation. Sequoia led the round, with Nvidia, Bessemer Venture Partners and Bain Capital Ventures participating, TechCrunch reported on November 2.
The company builds a continual-learning platform: businesses start from an open source model, connect it to their applications, collect feedback on how it performs, and Trajectory retrains the model so it improves at specialised tasks. TechCrunch framed the pitch directly against today's common approach of fine-tuning models with internal research teams or expensive consultants, which it described as forward-deployed engineers. Harvey and Rogo already use it to post-train open source models, and other listed customers include Decagon, Mercor and Nvidia. The platform is now publicly available.
For FDEs, this is a useful signal about where the job is heading. Vendors are starting to productise the model-adaptation part of deployment work and put it in the hands of domain experts like lawyers and analysts. That doesn't remove the need for people who scope workflows, wire systems together and get adoption inside a customer, but it suggests FDE value will lean further toward integration, evaluation design and business outcomes rather than hand-tuning models.
Source: techcrunch.com