Member of Technical Staff

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Ricursive Intelligence
Full time
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USA
11/06/2026
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Palo Alto, CA

Responsibilities

Design and iterate on SFT and RL recipes for large-scale models.
Build and scale synthetic-data generation, filtering, and coverage-metrics pipelines.
Create long-horizon synthetic environments and task curricula for agentic coding and reasoning.
Improve RL stability at scale through recipe and stability work.
Build evaluations that track agentic capability, detect regressions, and guide recipe decisions.
Own end-to-end experiments on large GPU clusters, including launching, debugging, and analyzing runs.
Partner with teammates to improve post-training efficiency on training and serving platforms.

Requirements

Bachelor’s, master’s, or doctoral degree in computer science, electrical engineering, mathematics, or a closely related field, or equivalent practical experience.
Hands-on post-training experience with SFT, RL, synthetic data, and environments at a frontier AI lab or equivalent.
Deep understanding of transformer internals and distributed training.
A shipped post-training run, model release, or research outcome that improved a benchmark or product.
Preferred: research record in RL, NLP, or machine learning, including publications at venues such as NeurIPS, ICML, ICLR, or COLM.
Preferred: experience building RL environments for complex frontier coding and agentic benchmarks.
Preferred: experience shipping synthetic-data generation and filtering at scale.
Preferred: experience shipping RL infrastructure, including asynchronous rollouts and inference engines such as vLLM or SGLang.
Preferred: ability to navigate ambiguity and make design decisions across the stack.
Interest in applying LLMs to hardware and chip design; prior experience is not required.

Ricursive Intelligence

Ricursive Intelligence builds AI systems that co-optimize machine learning models and the chips that run them, starting with AI-driven chip design tools for semiconductor and AI hardware teams. Its business focuses on software and research that closes the loop between model performance and silico...

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