Research Scientist - Agent Systems

Optimized, Inc.; San Francisco (CA); On-site; USD 180,000 - 250,000; Full time

Job summary

Optimized, Inc. is seeking a research scientist in San Francisco to advance the intelligence of supply chain agents. Responsibilities include developing planning approaches, designing experiments, publishing findings, and collaborating with ML engineers. Candidates should have a PhD or equivalent experience in ML or NLP, along with research publications in LLMs or agent systems. The compensation range for this role is $180,000 - $250,000 plus equity.

Qualifications

• PhD or equivalent research experience in ML, NLP, or a related field.
• Published work on LLMs, agent systems, planning, or multi-agent coordination.
• Experience building research prototypes in Python.

Responsibilities

• Research and develop novel approaches to agent planning.
• Design experiments to evaluate agent reliability and accuracy.
• Collaborate with ML engineers to translate research into production.

Skills

Research in ML, NLP

Python programming

Multi-agent coordination

Planning under uncertainty

Education

PhD or equivalent research experience

Job description

We're looking for a research scientist to advance the core intelligence of our supply chain agents. You'll work on the fundamental problems of multi-agent coordination, planning under uncertainty, and grounding LLM reasoning in real-world supply chain constraints, then ship your research into production.

Compensation range for this role is $180,000 - $250,000 + equity.

What you'll do:

• Research and develop novel approaches to agent planning, tool use, and multi-step reasoning for supply chain optimization
• Design experiments to evaluate agent reliability, accuracy, and decision quality against real-world procurement outcomes
• Publish findings internally and externally; we believe in contributing to the broader research community
• Collaborate with ML engineers to translate research breakthroughs into production-grade agent capabilities
• Stay at the frontier of LLM research, agent architectures, and reinforcement learning from human feedback

You may be a good fit if you:

• Have a PhD or equivalent research experience in ML, NLP, or a related field
• Have published work on LLMs, agent systems, planning, or multi-agent coordination
• Are comfortable building research prototypes in Python and iterating quickly
• Care about real-world impact, and you want your research to ship, not just get cited
• Can work across the stack when needed, from model fine-tuning to evaluation infrastructure
• Are excited about applying AI to complex, messy, real-world optimization problems