Staff Machine Learning Engineer (AI)

Location: San Francisco, California, United States. Hybrid R&D role with two days per week in the San Francisco office; the company also has meaningful office presence in New York.

Company

hirify.global builds an AI automation platform for healthcare organizations, including hospitals, health systems, pharmacies, and payors.

What you will do

• Define production agent architecture covering tool use, retrieval, context management, routing, orchestration, tracing, and failure recovery.
• Train and ship LLMs and vision-language models for healthcare tasks and establish reusable engineering patterns.
• Set the AI quality strategy through automated graders, LLM judges, regression suites, human review, failure taxonomies, and offline-to-online validation.
• Build systems that turn production failures and user feedback into improved datasets, evaluations, and models.
• Set standards for dataset versioning, observability, MLOps, privacy, and reproducibility.
• Co-own the 12-month ML roadmap, lead technical reviews, mentor senior engineers, and support hiring.

Requirements

• 8+ years of experience in machine learning or software engineering, including 3+ years leading LLM or agentic systems in production.
• End-to-end applied post-training experience with supervised fine-tuning, preference optimization, distillation, or reinforcement learning.
• Experience designing evaluation strategies for LLM or agent systems and using results to make production release decisions.
• Production agent architecture experience, including LangGraph, RAG, and vector search such as Pinecone.
• Strong Python and PyTorch skills, with experience in distributed training and serving using tools such as vLLM, FSDP, or DeepSpeed.
• Strong MLOps experience with Docker, Kubernetes, MLflow or equivalent, CI/CD, and experiment and dataset tracking.

Nice to have

• Healthcare data experience with EHR, FHIR, claims, or prior authorization.
• Publications or open-source work in LLM evaluation, post-training, or agents.
• Experience interviewing and hiring machine learning engineers.
• Experience working in another regulated, high-stakes domain.
• Open-source contributions, technical write-ups, talks, or evaluations used by others.

Culture & Benefits

• Mission-driven team focused on improving healthcare operations and patient outcomes.
• Flexible hybrid work environment with a remote-first approach and meaningful office presence.
• Medical, dental, and vision insurance for employees and family participation.
• 401(k) with a company match, equity for full-time employees, and unlimited PTO.
• Parental leave, wellness and commuter stipends, and a daily lunch stipend.

Hiring process

• Within the first 30 days, review the agents, evaluations, and training pipeline and identify the three biggest gaps.
• Within 90 days, ship a post-training or evaluation improvement that moves a production metric and publish the plan for the following two quarters.