Acceler8 Talent
Senior LLM Research Scientist
Location:
SF Bay Area (On-site)
Base Pay Range $200,000.00/yr – $250,000.00/yr
We’re representing a cutting‑edge AI organization building autonomous systems that can reason, plan, and interact with the physical world. This is a chance to join a small, elite research team pushing the frontier of LLM‑driven autonomy.
The Opportunity You’ll lead research that powers intelligent agents capable of long‑horizon reasoning, structured tool‑use, and real‑world decision‑making. This is deep ML research with immediate, tangible impact.
What You’ll Work On
Building LLM reasoning and planning frameworks
Designing structured tool‑use, memory, reflection, and multi‑step workflows
Developing safe, robust policies for autonomous systems
Training and evaluating models across real engineering and scientific tasks
Collaborating with systems, simulation, and infrastructure experts
What We’re Looking For
Strong research background in LLMs, reasoning, or agents
Experience with SFT, RLHF/DPO, verifier‑guided RL, or related training techniques
Ability to design and evaluate long‑horizon behaviors
Comfort working in a fast, interdisciplinary R&D environment
Why Join
Build models that control
real
physical‑world systems, not just simulations
Massive scope for autonomy, creativity, and impact
Competitive compensation + meaningful ownership
#J-18808-Ljbffr
SF Bay Area (On-site)
Base Pay Range $200,000.00/yr – $250,000.00/yr
We’re representing a cutting‑edge AI organization building autonomous systems that can reason, plan, and interact with the physical world. This is a chance to join a small, elite research team pushing the frontier of LLM‑driven autonomy.
The Opportunity You’ll lead research that powers intelligent agents capable of long‑horizon reasoning, structured tool‑use, and real‑world decision‑making. This is deep ML research with immediate, tangible impact.
What You’ll Work On
Building LLM reasoning and planning frameworks
Designing structured tool‑use, memory, reflection, and multi‑step workflows
Developing safe, robust policies for autonomous systems
Training and evaluating models across real engineering and scientific tasks
Collaborating with systems, simulation, and infrastructure experts
What We’re Looking For
Strong research background in LLMs, reasoning, or agents
Experience with SFT, RLHF/DPO, verifier‑guided RL, or related training techniques
Ability to design and evaluate long‑horizon behaviors
Comfort working in a fast, interdisciplinary R&D environment
Why Join
Build models that control
real
physical‑world systems, not just simulations
Massive scope for autonomy, creativity, and impact
Competitive compensation + meaningful ownership
#J-18808-Ljbffr