Member of Technical Staff, Post-Training
Join a pioneering AI lab as a post-training researcher to innovate in chip design and AI model efficiency.
Posted by employer 9 hours ago
First seen on Joblaze 3 hours ago
Last verified on the company career page 3 hours ago
Skills & Technologies
What you'll build
- Design and iterate on SFT and RL recipes
- Build and scale synthetic data pipelines
- Create long-horizon synthetic environments
- Make RL stable at scale
- Own experiments end to end on large GPU clusters
Must have
- Degree in CS, EE, math, or related field
- Hands-on post-training experience
- Deep fundamentals in transformer internals
Nice to have
- Research track record in RL, NLP, or machine learning
- Experience building RL environments
- A shipped result in post-training
- Shipped RL infrastructure
- Interest in applying LLMs to hardware
AI in the day-to-day
We built our own synthetic data pipelines instead of buying post-training data.
Requirements
- Education
- Bachelor's degree
Not disclosed in this posting: compensation, years of experience, work arrangement, visa sponsorship.
Joblaze summary
In this role, the individual will focus on designing and refining post-training methodologies for large-scale AI models, emphasizing compute efficiency and stability in reinforcement learning. Key skills include hands-on experience with synthetic data generation, reinforcement learning, and a solid understanding of transformer architectures. This position is ideal for seasoned researchers or engineers with a strong background in AI, particularly those who have contributed to significant advancements in the field. Ricursive Intelligence offers a unique environment where top-tier talent collaborates on pioneering AI and chip design projects.
Joblaze insights
- Listed today — first seen on Joblaze November 6, 2026. Last confirmed on Ricursive Intelligence's careers page November 6, 2026.
- AI/ML appears in 34.2% of 269 comparable staff ai/ml roles in United States; synthetic data appears in 0.7% of 269 comparable staff ai/ml roles in United States.
Quick facts
- What's the tech stack?
- Joblaze extracted these technologies from the posting: AI/ML, Transformers, reinforcement learning, synthetic data.
- What seniority level is this role?
- Ricursive Intelligence targets staff-level candidates for this position.
- Is this full-time or contract?
- Full-time for this Member of Technical Staff, Post-Training role at Ricursive Intelligence.
From the original posting
We are looking for the very best post-training researchers and engineers in the world.
A small team trained Ripple-65B Preview from scratch and set a new U.S. open-weight state-of-the-art on agentic coding (80.5 on TerminalBench 2.1), outperforming U.S. open-weight models up to 15x larger at one-tenth the cost of the previous open-weight SOTA. We built our own synthetic data pipelines instead of buying post-training data, and reached state-of-the-art with 1.4M rollouts, roughly 70x fewer than the prior SOTA.
You will contribute to core pieces of post-training, from synthetic data to RL environments to evaluation, and your work will show up directly in the model. We built Ripple to start closing the loop between models and the chips they run on. Few teams work at this intersection, and we've only scratched the surface. As we scale toward the frontier, we are selectively expanding our team of brilliant researchers and engineers.
WHAT YOU WILL DO
Design and iterate on SFT and RL recipes for large-scale models, with a focus on compute efficiency and steep, stable RL climbs.
Build and scale synthetic data pipelines, including generation, quality filtering, and coverage metrics.
Create long-horizon synthetic environments and task curricula for agentic coding and reasoning.
Make RL stable at scale through careful recipe and stability work.
Build evaluations that track agentic capability, catch regressions, and drive recipe decisions.
Own experiments end to end on large GPU clusters: design, launch, debug, and analyze runs.
Partner with teammates to make post-training efficient on our training and serving platforms.
MINIMUM QUALIFICATIONS
Degree in CS, EE, math, or a related field, or equivalent practical experience.
Hands-on post-training experience (SFT, RL, synthetic data and environments) at a frontier AI lab or equivalent.
Deep fundamentals in transformer internals and distributed training.
PREFERRED QUALIFICATIONS
Research track record in RL, NLP, or machine learning, such as publications at NeurIPS, ICML, ICLR, or COLM.
Experience building RL environments for complex frontier coding and agentic benchmarks.
A shipped result: a post-training run, data recipe, model release, or research outcome that moved a benchmark or product.
Shipped RL infrastructure, such as async rollouts and inference engines like vLLM or SGLang.
Experience navigating ambiguity and making design decisions across the stack.
Interest in applying LLMs to hardware and chip design (no prior experience required).
Standard company text repeated across Ricursive Intelligence's postings is omitted here.