Machine Learning Researcher

About The Company

Opti is building AI-native IAM solutions for enterprises. Our platform automatically discovers, understands, and manages access relationships across organizations - solving IAM problems that traditional tools can't touch. We recently launched from stealth with $20M in seed funding and are working with enterprises across industries.

About The Role

Opti is growing its AI team and is looking for a hands-on Machine Learning Researcher who loves turning ideas into real systems.

In this role, you’ll work at the intersection of research and production, building intelligent solutions around LLMs, agentic systems, reasoning pipelines, RAG architectures, and classic ML models.

If you enjoy deep algorithmic thinking, fast experimentation, and seeing your work make an impact in real products, this role is for you.

What You’ll Do

Research, prototype, and improve LLM-based agents and reasoning workflows

Design and build ML models (supervised, unsupervised, anomaly detection, etc.)

Develop retrieval and RAG components and optimize end-to-end pipelines

Design data pipelines for ingestion, feature engineering, experimentation, and evaluation

Run experiments, analyze results, and iterate quickly

Translate research ideas into clean, production-ready code

Collaborate closely with engineering teams to deploy models into live systems

Requirements

• MSc or PhD in Computer Science, Machine Learning, Mathematics, or a related field
  
  

• ~3–5 years of experience in ML research or applied ML
  
  

• Strong algorithmic and mathematical foundations
• Hands-on experience with LLMs, agentic workflows, context management, and/or RAG systems
• Strong Python skills and experience with ML tooling and data processing frameworks
• Ability to independently design experiments, evaluate models, and debug complex systems
  
  

• Experience with retrieval systems, vector databases, graph data, or orchestration frameworks
• Background in reinforcement learning, anomaly detection, or time-series modeling
• Contributions to research, patents, or open-source ML/LLM projects