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Founding Applied Research Engineer
Job in
San Francisco, San Francisco County, California, 94199, USA
Listed on 2026-07-25
Listing for:
Lucidic AI
Full Time
position Listed on 2026-07-25
Job specializations:
-
Software Development
AI Engineer (Applied/Software), AI Reliability/ Performance Engineer, Machine Learning/ ML Engineer
Job Description & How to Apply Below
Overview
Join Lucidic AI as a Founding Applied Research Engineer to develop and deploy cutting‑edge optimization algorithms for enterprise AI agents. You will work directly with customers to understand their agent systems and build solutions that measurably improve accuracy, reliability, and performance.
LocationSan Francisco, CA
Full-time, On-site
What You'll Do- Design and implement optimization algorithms (RL, genetic algorithms, search‑based methods) for agent systems
- Work directly with enterprise customers to understand their agent architectures and pain points
- Build evaluation frameworks and benchmarks to measure agent performance improvements
- Collaborate with the product team to translate research insights into scalable product features
- Publish technical blog posts and contribute to the broader AI community
- Help define the technical roadmap for Lucidic's optimization platform
- Strong background in machine learning, reinforcement learning, or optimization
- Experience building and deploying ML systems in production
- Proficiency in Python and modern ML frameworks (PyTorch, JAX, etc.)
- Excellent communication skills and ability to work directly with customers
- Self‑motivated with ability to thrive in an early‑stage startup environment
- Experience with LLM prompt optimization or agent systems
- Publications in top ML venues (NeurIPS, ICML, ICLR, etc.)
- Prior startup experience or entrepreneurial background
- Experience with distributed systems and cloud infrastructure
We build tooling that automatically evaluates and improves enterprise AI agents using search + learning loops across prompts, tools, and policies. Our customers care about reliability, auditability, and measurable gains.
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