Overview

Work on advanced AI and ML systems for clinical applications.

Responsibilities

• Design, build, and maintain production-grade AI and machine learning systems tailored to clinical use cases.
• Develop and deploy autonomous AI agent systems that operate on real-world clinical data, including tool usage and self-correction capabilities.
• Integrate at least two large language models (such as Gemini, GPT-4, Llama, Mistral, or equivalent) into production software environments.
• Implement fine-tuning workflows, including LoRA, QLoRA, PEFT, instruction tuning, and domain adaptation for clinical AI models.
• Design and manage LLM evaluation pipelines, including benchmark design and continuous performance monitoring for clinical scenarios.
• Architect and implement multi-agent systems using orchestrator–worker patterns, tool-calling loops, and self-correcting agent pipelines.
• Collaborate with cross-functional stakeholders to translate clinical and business requirements into AI engineering specifications and solutions.
• Ensure robustness, reliability, and safety of AI systems in production, including monitoring, troubleshooting, and iterative improvement.
• Document system architectures, design decisions, and operational procedures to support maintainability and knowledge sharing.
• Contribute to continuous improvement of AI engineering practices, tools, and frameworks within the team.
• At least 8 years of hands-on AI and machine learning engineering experience focused on building, deploying, and maintaining AI systems in production (not solely data science or research).
• Proven experience delivering at least one autonomous AI agent system that operates on real data and uses tools with self-correction capabilities.
• Demonstrated experience integrating at least two large language models (for example, Gemini, GPT-4, Llama, Mistral, or equivalent) into production software.
• Strong expertise in fine-tuning techniques, including LoRA, QLoRA, PEFT, instruction tuning, and domain adaptation for LLMs.

Qualifications

• Hands-on experience architecting multi-agent systems, including orchestrator–worker patterns, tool-calling loops, and self-correcting agent pipelines.
• Solid software engineering skills for AI/ML, including production deployment, monitoring, and maintenance of AI systems.
• Background in clinical AI or experience applying AI/ML techniques in clinical or healthcare-related contexts.
• Understanding of best practices for safe, reliable, and responsible deployment of LLMs in sensitive domains such as healthcare.
• Ability to design and refine benchmarks that reflect real-world clinical performance requirements.
• Strong communication skills to explain complex AI systems to technical and non-technical stakeholders.
• Collaborative mindset and ability to work effectively in cross-functional teams.
• Hiring diverse talent
• Maintaining an inclusive environment through persistent self-reflection
• Building a culture of care, engagement, and recognition with clear outcomes
• Ensuring growth opportunities for our people

Nice to have

• Background in clinical AI
• Strong communication skills

Education

• Bachelor's Degree in a related field

Benefits

• Work with cutting-edge AI technologies
• Diversity and inclusion focus