Machine Learning Engineer (Healthcare)

Location: San Francisco, United States; on-site. Work together in person, spending most of the week in the office.

Base salary: $225,000–$300,000+ per year, plus meaningful equity.

Company

hirify.global Health builds AI systems that combine population-level clinical knowledge with longitudinal patient history to deliver personalized, verifiable reasoning in real clinical workflows.

What you will do

• Own the architecture, data, modeling, evaluation, and production infrastructure of end-to-end ML systems.
• Train and fine-tune large language models for clinical reasoning, medical question answering, and evidence-grounded generation.
• Drive ambiguous ML problems from initial formulation through reliable production deployment and set technical direction.
• Develop evaluation frameworks for model safety and clinical validity.
• Integrate ML systems into product workflows and patient-facing applications.
• Monitor production performance and define correctness for ambiguous clinical workflows with engineers and clinicians.

Requirements

• Strong foundation in machine learning and software engineering.
• Proven experience building and owning production ML systems where performance, reliability, or correctness materially mattered.
• Experience taking ambiguous ML problems from 0 to 1, including problem formulation, model design, and productionization.
• Hands-on experience with PyTorch or similar frameworks.
• Ability to work independently in high-ambiguity environments with strong product and engineering judgment.
• Availability to work on-site in San Francisco and in person most of the week.

Nice to have

• Experience deploying LLMs in production.
• Experience building distributed systems or large-scale data pipelines.
• Experience with clinical, biomedical, or other regulated datasets.
• Experience working on systems with real-world consequences, such as healthcare, finance, or infrastructure.

Culture & Benefits

• Work on high-stakes systems with direct impact on patient care.
• Build systems that shape how AI is trusted in clinical decision-making.
• Take significant ownership in a small, high-caliber engineering and research team.
• Competitive compensation and meaningful equity in an early-stage Series A company.