Research Engineer - Agents

Optimized, Inc.; San Francisco (CA); On-site; USD 175,000 - 240,000; Full time

Job summary

Optimized, Inc. is seeking a Research Engineer to bridge the gap between research and production in AI systems. You will be responsible for turning prototypes into production-ready capabilities, building data pipelines, and optimizing operations. The ideal candidate should have over 3 years of experience with ML/AI systems and be fluent in Python.

The compensation range for this role is $175,000 - $240,000 plus equity, and is aimed at solving real-world industrial problems.

Qualifications

• 3+ years building production ML or AI systems from prototype to deployment.
• Hands-on experience with LLMs, including fine-tuning and evaluation.
• Fluency in Python and familiarity with modern ML tooling.

Responsibilities

• Bridge research and production: turn research ideas into production-ready agent capabilities.
• Own data pipelines, fine-tuning, and experimentation infrastructure.
• Build evaluation harnesses and tooling to measure improvements.
• Drive down latency and cost without sacrificing reliability.
• Integrate production feedback into research and improvement.

Skills

Production ML/AI systems

Hands-on experience with LLMs

Fluent in Python

Modern ML tooling (PyTorch, HuggingFace)

Evaluation and optimization skills

Job description

Optimized deploys AI agents into the most critical supply chains in the world: the operations of Fortune 500 manufacturers and government organizations. Getting a promising research idea to run reliably in those environments is its own hard problem, and we need a research engineer to own the path from prototype to production.

As a Research Engineer on agents, you'll work at the seam between research and engineering, building the training stack, evaluation harnesses, and inference and serving systems that move ideas from notebook to deployment. You'll turn fragile prototypes into production agent capabilities customers depend on, and turn production signals back into the next round of research.

Compensation range for this role is $175,000 - $240,000 + equity.

What you'll do

• Bridge research and production: You'll take promising research ideas and turn them into robust, production-ready agent capabilities customers depend on.
• Build the training stack: You'll own the data pipelines, fine-tuning, and experiment infrastructure that let researchers iterate quickly.
• Make evaluation fast: You'll build the eval harnesses and tooling that measure whether a change actually improves agent quality.
• Optimize for production: You'll drive down latency and cost across model providers without sacrificing reliability.
• Close the loop: You'll turn production signals and failures back into the next round of research and improvement.

What you'll bring

• Have 3+ years building production ML or AI systems, including owning models from prototype to deployment
• Have hands‑on experience with LLMs (fine‑tuning, retrieval‑augmented generation, and evaluation)
• Are fluent in Python and modern ML tooling (PyTorch, HuggingFace, or similar)
• Are rigorous about evaluation, and you measure before you optimize
• Enjoy working at the seam between research and engineering
• Are excited about applying AI to real‑world industrial problems, not just benchmarks