Come help us scale @harvey’s model training team. If you’re interested in bringing frontier agent research into the Harvey product and working with: - @baseten to scale up RL to 80M+ token virtual datarooms - @PrimeIntellect to create structured agent training environments from unstructured legal data - @FireworksAI_HQ to navigate the quality <> cost Pareto frontier with inference-time routing and advisor models - @LangChain & LangChain Labs to build efficient verifiers and close the observability <> training feedback loop - @appliedcompute to post-train open weight models and high-volume agents for end-to-end legal tasks - @EngramLab to create an entire synthetic law firm and firm knowledge memory systems for better / more efficient open-world search - @trajectorylabs & @NVIDIAAI to shape the frontier of continual learning and sovereign AI for high-stakes domains - @mercor & @SnorkelAI to build out Legal Agent Bench and other benchmarks across legal and other verticals and other projects like this, then this is the role for you. Apply here: