Research Engineer, Core ML
Together AI · San Francisco, CA · Research
About this role
Together AI is hiring a mid-level Research Scientist in the machine learning function based in San Francisco, CA. The posting calls out experience with Python, LLMs, Reinforcement Learning, Distributed Systems. Listed education preference: a bachelor's degree or equivalent. Compensation is listed at $200,000–$280,000 per year.
- Role
- Research Scientist
- Function
- machine learning
- Level
- mid
- Track
- Individual contributor
- Employment
- Full-time
- Location
- San Francisco, CA
- Education
- Bachelor's degree
- Department
- Research
More roles at Together AI
Job description
from Together AI careersAbout the Role
This is a research engineering role with direct production impact. You won’t be publishing ideas in isolation—you will translate new RL algorithms, scheduling methods, and inference optimizations into production-grade systems that power Together’s API. Success in this role means shipping measurable improvements in latency, throughput, cost, and model quality at scale. We are looking for researchers who enjoy owning systems end-to-end and turning frontier ideas into robust infrastructure.
The Core ML (Turbo) at Together AI team sits at the intersection of efficient inference (algorithms, architectures, engines) and post‑training / RL systems. We build and operate the systems behind Together’s API, including high‑performance inference and RL/post‑training engines that can run at production scale.
Our mandate is to push the frontier of efficient inference and RL‑driven training: making models dramatically faster and cheaper to run, while improving their capabilities through RL‑based post‑training (e.g., GRPO‑style objectives). This work lives at the interface of algorithms and systems: asynchronous RL, rollout collection, scheduling, and batching all interact with engine design, creating many knobs to tune across the RL algorithm, training loop, and inference stack. Much of the job is modifying production inference systems—for example, SGLang‑ or vLLM‑style serving stacks and speculative decoding systems such as ATLAS—grounded in a strong understanding of post‑training and inference theory, rather than purely theoretical algorithm design.