Skip to main content

Clera

AI/LLM Engineer

San Francisco$90k–$200kfulltimemidAdded today

About this role

A Y Combinator-backed B2B SaaS startup in sales automation seeks an AI/LLM Engineer to design and deploy intelligent features end-to-end, from data pipelines and LLM fine-tuning to production systems. You'll own full-stack AI agent development with direct product impact in a small, senior team.

What you'll do

  • Design, build, and deploy AI agents across the full stack with end-to-end ownership
  • Fine-tune LLMs and work with embeddings for classification and reranking tasks
  • Implement RLHF and DPO techniques to align models with human feedback
  • Build and scale data pipelines to process large-scale unstructured data
  • Optimize algorithms for product search and matching
  • Improve backend scalability, stability, and performance

What they're looking for

  • LLMs and embeddings
  • RLHF and DPO techniques
  • NLP and machine learning
  • React, TypeScript, and Next.js
  • Data pipeline design and optimization
  • Production AI systems
  • Docker and Kubernetes
  • Cloud infrastructure (AWS, Azure, or GCP)

Benefits

  • Salary: $90,000–$200,000 USD annually
  • Visa sponsorship available
  • Early-stage equity at a well-funded startup
  • High engineering ownership and product influence
  • On-site role in Munich with senior team
Apply with Autofill

Opens the application — the Jobs AI extension fills it for you. Set up autofill

Opens the official application on the employer’s site. No login required.

Clera

Clera builds an agentic operating system that automates complex workflows and processes through AI agents, with a platform designed to simplify distributed infrastructure management for developers. The company is hiring Founding Engineers, Customer Engineers, and Product Engineers to develop both backend systems and user-facing interfaces across their AI automation products.

View all jobs at Clera

Likely interview questions

  • Can you walk us through a production LLM system you've built—what were the key optimization decisions you made?
  • Describe your experience with RLHF or DPO. How did you measure alignment success?