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Further AI

Forward Deployed Engineer

San Francisco$140k–$225kfulltimemidAdded 1 month ago

About this role

FurtherAI is seeking a Forward Deployed Engineer to build enterprise AI agents for the insurance industry. You'll work in-person in San Francisco with a world-class team backed by top investors, directly engaging customers and implementing cutting-edge AI solutions at scale.

What you'll do

  • Develop enterprise-grade AI agents from concept to production
  • Collaborate with the CTO and engineering team on AI/ML product implementation
  • Engage directly with customers to understand workflows and translate them into technical solutions
  • Build and optimize backend systems supporting agentic capabilities
  • Deploy cutting-edge AI features in production environments
  • Contribute to product direction and strategy as an early team member

What they're looking for

  • Backend systems development (Python)
  • AI/machine learning implementation
  • Consumer-facing product engineering
  • Customer discovery and communication
  • Scalable systems design
  • Written and verbal communication
  • Product ownership mindset
  • Problem-solving under fast-paced conditions

Benefits

  • Comprehensive health, dental, and vision coverage
  • Competitive salary with meaningful stock options
  • Unlimited PTO
  • On-site meals (lunch and dinner)
  • 401(k) with company contribution
  • Pre-tax commuter benefits
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Further AI

Further AI builds enterprise AI agents designed specifically for the insurance industry. The company is hiring Forward Deployed Engineers and Solutions Engineers to implement and optimize AI-powered solutions directly with insurance customers.

View all jobs at Further AI

Likely interview questions

  • Walk us through a time you built a consumer-facing feature from scratch. How did you gather customer requirements, and what challenges did you face in translating them into a technical solution?
  • Tell us about your experience with Python backend systems. What's the most complex or scalable system you've built, and how did you approach performance optimization?