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Serval

Software Engineer, Applied AI

San Francisco$200k–$325kfulltimemidAdded 3 weeks ago

About this role

Serval seeks an Applied AI Engineer to develop intelligent agents that automate enterprise workflows using large language models and cutting-edge AI techniques. You'll design, build, and deploy production AI systems that transform repetitive operational processes across IT, HR, Finance, and other business functions.

What you'll do

  • Design and deploy AI-powered features leveraging LLMs and foundation models
  • Develop and optimize applied AI systems including model selection, fine-tuning, and evaluation
  • Integrate AI capabilities into production environments with focus on reliability and scalability
  • Collaborate with engineering and product teams to deliver customer-facing experiences
  • Evaluate model performance and iterate based on data and user feedback
  • Establish AI engineering best practices and raise technical standards

What they're looking for

  • Production AI/ML systems development
  • Large language model and foundation model expertise
  • Prompt engineering and LLM fine-tuning
  • API integration and data pipeline design
  • Cloud platforms (AWS, GCP, or equivalent)
  • Software engineering fundamentals
  • Product-focused engineering mindset
  • Go, TypeScript, or Python

Benefits

  • Direct impact on product strategy and company success
  • Opportunity to build AI products from zero-to-one
  • Work with experienced team and top-tier investors (Sequoia, Redpoint, etc.)
  • Fast-paced startup environment with ownership and accountability
  • Collaborative culture focused on innovation
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Serval

Serval builds an AI-native enterprise automation platform that enables intelligent workflow automation for sophisticated IT and security buyers. The company is hiring for technical pre-sales, infrastructure engineering, and security leadership roles to scale its cloud and self-hosted deployments while establishing comprehensive security foundations across its multi-tenant platform.

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Likely interview questions

  • Walk us through a production AI system you've built using LLMs or foundation models. How did you approach model selection, and what challenges did you face in deployment?
  • Describe your experience with prompt engineering and/or fine-tuning. How do you evaluate whether these techniques are actually improving your model's performance for a specific use case?
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