Amigo

Amigo

Applied AI Tech Lead

San Francisco · Staff+ · Full-time

Sponsorship not specifiedDetected 20 days ago
Machine LearningLLMsLeadershipCollaborationMentoring

About the role

  • It's a hands-on leadership role at the intersection of engineering, product, and the customer.
  • Depending on your strengths, the role leans toward deep technical leadership, people leadership, or both.
  • Leading an AI deployment for a major healthcare customer, from first conversation to production

Responsibilities

  • You'll set the architecture, break big ambiguous problems into work a team can own, commit the timelines, and hold the bar for what ships.
  • Others manage a handful of engineers and own how the team operates.
  • Making the calls on tradeoffs: what to build now, what to reuse, and what to push back on
  • You've broken hard, ambiguous problems into work other engineers could own and deliver

Requirements

  • You have real experience with LLMs and agent systems, or the depth to get there fast
  • You have the judgment to make tradeoffs and the standing to say no when something doesn't hold up
  • You can work on site in San Francisco

Nice to have

  • Experience shipping production AI or ML systems
  • Background in customer-facing or forward-deployed engineering
  • Experience growing engineers or running a small team
  • Daily catered lunch and dinner
  • Conference attendance budget for professional development
  • Academic collaboration opportunities
  • Patients Win, We Win
  • If patients aren't getting better care, we haven't earned the right to scale.

Compensation

  • Annual team offsite

Benefits

  • Comprehensive health, dental, and vision insurance
  • Mental health support and wellness coaching
  • Flexible wellness stipend for fitness, therapy, or personal growth
  • Annual learning budget for courses, books, or conferences

Company info

  • Working directly with customers to keep scope, timelines, and quality honest

This listing is sourced directly from Amigo's careers page and normalized into a canonical job model.