Staff Software Engineer, Applied AI (Healthcare)

Location: Burlingame, CA, United States; onsite

Base salary: $200,000–$275,000, depending on experience and qualifications; equity and benefits are also offered.

Company

hirify.global builds scalable AI-powered healthcare solutions that optimize revenue cycle management.

What you will do

• Architect and implement scalable, low-latency APIs and services that wrap, orchestrate, and optimize LLMs for healthcare use cases.
• Build ingestion, preprocessing, and retrieval-augmented generation pipelines for clinical and revenue-cycle data.
• Design LLMOps and observability systems for model monitoring, evaluation, cost tracking, guardrails, reliability, and responsible use.
• Optimize LLM workloads through caching, batching, load balancing, and cloud-native scaling across containerized environments.
• Implement HIPAA-ready infrastructure, data governance, and auditability for LLM-powered applications.
• Lead end-to-end LLM backend delivery, collaborate with product, ML, and healthcare experts, establish engineering practices, and mentor peers.

Requirements

• 5+ years of backend or full-stack software engineering experience, including 3+ years working on ML/LLM-enabled applications.
• Strong Python skills and ideally one statically typed language such as Go, Java, or TypeScript.
• Experience with LLM integration frameworks and APIs, including Hugging Face, LangChain, LlamaIndex, OpenAI APIs, or Anthropic.
• Deep knowledge of distributed systems, service-oriented architecture, and building APIs at scale.
• Cloud-native expertise with AWS, GCP, or Azure, plus Kubernetes, Docker, or Terraform.
• Familiarity with MLOps/LLMOps practices, including model CI/CD, evaluation harnesses, monitoring, and reproducibility.
• Excellent system design skills and the ability to align technical architecture with product goals.

Nice to have

• Experience applying LLMs in healthcare or other regulated industries, including FHIR, HL7, or HIPAA.
• Hands-on experience with RAG pipelines, vector databases, and structured-output orchestration.
• Experience with enterprise SaaS or mission-critical platforms where uptime, latency, and scale matter.
• Knowledge of responsible AI, safety, and privacy-preserving ML techniques.

Culture & Benefits

• Leadership role with ownership of core ML/LLM systems and influence on technical direction.
• Opportunity to apply AI to healthcare and revenue-cycle workflows.
• Equity options and health, dental, and vision coverage.