Skip to main content
Loading
loadingbar
Loading, Please wait..!!

Research Scientist — Agentic AI & Multimodal RL

  • Job type Posted on: Jul 22, 2026
  • Experience level Simular
  • Employment type Palo Alto, California
  • Employment type Onsite
  • Salary Full-time

Point Apply Here APPLY LATER

Curious about compensation?

Explore the historical salary trends, average pay, and estimated compensation for Research Scientist — Agentic AI & Multimodal RL roles in California.

View Salary Guide →

Job Title :

Research Scientist — Agentic AI & Multimodal RL

Job Type :

Full-time

Job Location :

Palo Alto California United States

Remote :

No

Jobcon Logo Job Description :

Simular is seeking a Research Scientist to push the boundaries of AI research across planning, reinforcement learning, and multimodal reasoning. You will drive end-to-end experiments, from data collection to model evaluation, and collaborate with engineers to bring research prototypes into production. The role emphasizes publishing results at leading conferences and advancing methods through dataset construction and benchmarking against state-of-the-art models, with a strong focus on real-world #J-18808-Ljbffr

Jobcon Logo Position Details

Posted:

Jul 22, 2026

Reference Number:

14660_8EB7602C32040FA1313B0D4A225CFCBF

Employment:

Full-time

Salary:

Not Available

City:

Palo Alto

Job Origin:

APPCAST_CPC

Share this job:

  • linkedin

Jobcon Logo
A job sourcing event
In Dallas Fort Worth
Aug 19, 2017 9am-6pm
All job seekers welcome!

Research Scientist — Agentic AI & Multimodal RL    Apply

Click on the below icons to share this job to Linkedin, Twitter!

Simular is seeking a Research Scientist to push the boundaries of AI research across planning, reinforcement learning, and multimodal reasoning. You will drive end-to-end experiments, from data collection to model evaluation, and collaborate with engineers to bring research prototypes into production. The role emphasizes publishing results at leading conferences and advancing methods through dataset construction and benchmarking against state-of-the-art models, with a strong focus on real-world #J-18808-Ljbffr

Loading
Please wait..!!