Transform Healthcare with Generative AI
The Role
Required Qualifications
Preferred
Culture & Environment
Compensation & Benefits
Visa
Compensation
Our client is building the safety-critical infrastructure powering Generative AI in healthcare.
They provide AI governance guardrails, healthcare-specific agent creation, and real-time algorithm monitoring that enables major health systems to deploy AI safely and at scale. Their platform is already live in production environments, driving measurable impact.
They are seeking a Technical Lead / Platform Engineer to help architect and scale their GenAI SaaS platform.
As a senior member of the engineering team, you will:
- Define and implement the architecture of the AI SaaS platform
- Lead development of core backend and platform services
- Drive distributed systems and scalability decisions
- Write clean, production-grade code
- Own infrastructure automation, monitoring, and reliability
- Mentor engineers and guide technical direction
- Balance speed with long-term system design in a fast-moving startup
This role begins as a hands-on technical lead (IC + leadership) with clear growth toward broader technical ownership.
- 8+ years of relevant industry experience
- Background at a top-tier technology organization (FAANG or similarly engineering-focused company)
- Experience leading engineers and owning technical direction
- Production experience deploying and scaling a generative AI application
- Expert proficiency in Python
- Strong TypeScript or Java
- Experience with Go, Rust, or another modern compiled language
- Deep distributed systems expertise
- Strong microservices and REST API design experience
- Cloud expertise (GCP preferred; AWS or Azure acceptable)
- Infrastructure as code (Terraform)
- CI/CD, DevOps best practices, monitoring & observability
- Docker and Kubernetes
- Strong coding ability (hands-on leadership required)
- Startup mindset: high ownership, low ego, collaborative
- Significant GCP experience
- Multi-cloud exposure
- AI/ML frameworks (PyTorch, TensorFlow)
- Healthcare experience
- Familiarity with frontend frameworks
- Top-tier academic background
- Hybrid: 3 days per week in Palo Alto (Tues/Wed/Thurs)
- Flat, collaborative engineering culture
- High-growth, high-ownership environment
- Strong emphasis on both system design and hands-on coding
- Base salary: $220,000 – $280,000
- Equity package (details shared during process)
- Health insurance (~99% covered for employees, ~75% for dependents)
- Dental and vision insurance
- Developer-grade laptops and office equipment stipend
- Daily in-office meals (breakfast, lunch, dinner)
Compensation will vary based on experience and qualifications.
Our client can accommodate H-1B transfers only (I-140 approved or significant remaining term required).
If you are excited about building scalable AI systems that directly impact patient care, we would love to connect.
The base pay range for this role is $220,000 – $280,000 per year.
What you need to know about the San Francisco Tech Scene
San Francisco and the surrounding Bay Area attracts more startup funding than any other region in the world. Home to Stanford University and UC Berkeley, leading VC firms and several of the world’s most valuable companies, the Bay Area is the place to go for anyone looking to make it big in the tech industry. That said, San Francisco has a lot to offer beyond technology thanks to a thriving art and music scene, excellent food and a short drive to several of the country’s most beautiful recreational areas.
Key Facts About San Francisco Tech
- Number of Tech Workers: 365,500; 13.9% of overall workforce (2024 CompTIA survey)
- Major Tech Employers: Google, Apple, Salesforce, Meta
- Key Industries: Artificial intelligence, cloud computing, fintech, consumer technology, software
- Funding Landscape: $50.5 billion in venture capital funding in 2024 (Pitchbook)
- Notable Investors: Sequoia Capital, Andreessen Horowitz, Bessemer Venture Partners, Greylock Partners, Khosla Ventures, Kleiner Perkins
- Research Centers and Universities: Stanford University; University of California, Berkeley; University of San Francisco; Santa Clara University; Ames Research Center; Center for AI Safety; California Institute for Regenerative Medicine
