Clinical AI Engineer — Production Healthcare Platform

Raise; Austin (TX); Hybrid; USD 152,000 - 262,000; Full time

Benefits offered by this job

Top compensation

Flexible PTO

Health benefits

401(k) matching

Mission-driven team

Job summary

Raise is seeking an Applied AI Engineer/Scientist to build, evaluate, and improve clinical AI agents and supervised ML models for a healthcare AI platform. You will blend software engineering with ML research to convert frontier model capabilities into reliable production behavior.

Embedded in healthcare workflows, you’ll own problem framing, evaluation, deployment, and continuous improvement, partnering with research and ML engineering teams to advance model selection, fine-tuning, and

Qualifications

• 4+ years of software/ML engineering or applied AI experience.
• Strong Python skills and production-system experience.
• Experience turning messy real-world workflows into structured AI problems.
• Experience building evaluation systems and experiment tracking.
• Thrives in ambiguous, high-stakes domains with experts.

Responsibilities

• Design, build, and iterate on agentic AI systems for complex healthcare workflows.
• Develop long-horizon agent behavior across context construction, retrieval, tool use, memory, routing, verification, escalation, and human-in-the-loop review.
• Define success criteria and rubrics for clinical agents end-to-end.
• Build evaluation loops using logs, outputs, and benchmarks to measure performance and safety.
• Prototype and harden capabilities into auditable production systems with clear contracts and monitoring.
• Collaborate with research and ML engineering on model selection, fine-tuning, and instrumentation.

Skills

Python

Production systems

ML engineering

Evaluation

RAG & tools

Tools

Experiment tracking

Prompt management

Reproducible evaluation

Job description

Raise is seeking an Applied AI Engineer/Scientist to build, evaluate, and improve clinical AI agents and supervised ML models for a healthcare AI platform. You will blend software engineering with ML research to convert frontier model capabilities into reliable production behavior.

Embedded in healthcare workflows, you’ll own problem framing, evaluation, deployment, and continuous improvement, partnering with research and ML engineering teams to advance model selection, fine-tuning, and