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Campfire

AI Engineer

San Francisco$180k–$250kfulltimemidAdded 1 month ago

About this role

Campfire, a Y Combinator-backed accounting software startup, seeks an AI Engineer to design and deploy machine learning solutions that automate financial workflows and add intelligent features to their platform. You'll work in-office in San Francisco, taking ownership of AI projects from prototype to production in a fast-moving startup environment.

What you'll do

  • Design and implement AI-driven features for accounting automation and financial insights
  • Develop and fine-tune machine learning models using frameworks like PyTorch or TensorFlow
  • Deploy ML models to production and monitor their performance
  • Work with LLMs (e.g., OpenAI, Hugging Face) to build practical customer-facing applications
  • Collaborate cross-functionally with engineering, product, and design teams
  • Take end-to-end ownership from model prototyping through deployment

What they're looking for

  • Machine learning and deep learning
  • Natural language processing (NLP)
  • Python programming
  • PyTorch or TensorFlow
  • LLM fine-tuning and integration
  • ML model deployment and production systems
  • Rapid prototyping and iteration
  • Cross-functional collaboration

Benefits

  • 100% health insurance premium coverage for employees
  • 80% health insurance coverage for dependents
  • Comprehensive healthcare and dental options
  • Top decile benefits package
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Campfire

Campfire builds accounting software designed for startups and mid-market tech companies, with a focus on automating financial workflows through intelligent features. The company is hiring AI engineers and full-stack engineers to develop and deploy machine learning solutions and modern platform capabilities in their San Francisco office.

View all jobs at Campfire

Likely interview questions

  • Walk us through a machine learning model you deployed to production. What challenges did you face, and how did you monitor its performance after launch?
  • Describe your experience with LLMs like OpenAI or Hugging Face. Have you fine-tuned a model for a specific use case, and what was the outcome?
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