ExaSan Francisco, CAML Research Engineer trains foundational embedding models for web search, designs novel transformer architectures, creates datasets and evals, and beats state-of-the-art performance. Requires graduate-level ML expertise and ability to implement transformers in PyTorch.
Develop multi-agent AI architectures for enterprise coordination and collaborative reasoning. Requires research experience in MARL/GNNs, strong prototyping skills, and daily AI tool usage.
Designs and authors context, procedures, skills, and system prompts for AI agents performing autonomous accounting tasks. Ensures consistency, monitors performance, and shapes future capabilities through precise language and systems thinking.
Designs datasets and evaluation frameworks for frontier AI models, collaborating with top labs to expose failure modes, refine RLHF/RLVR pipelines, and measure data impact on capabilities. Requires strong quantitative skills, familiarity with LLM training, and research experience up to master's level.
Design and run SFT/RL experiments to measure dataset impact on LLM performance, capabilities, and alignment. Collaborate with labs to provide evidence of improvements; requires strong LLM training knowledge and fast experimentation, ideally undergrad/master's research background.
Conducts fundamental research on data-efficient ML architectures, including bootstrapped program synthesis and self-synthesizing learning systems. Requires Master's in ML/math, PyTorch fluency, and research experience.