Principal AI / Data Scientist

We are seeking a highly skilled Principal AI / Data Scientist to join a small, autonomous engineering team focused on advancing an AI-driven healthcare platform within a strictly regulated environment. This role demands a deep understanding of statistical and mathematical foundations combined with strong engineering expertise. The ideal candidate will be capable of taking AI challenges from concept through to production, working across data engineering, machine learning, large language models (LLMs), agents, model evaluation, inference, infrastructure, and deployment. You will collaborate closely with the founder and engineering team to enhance existing systems, identify weaknesses, and independently develop robust, scalable AI solutions that comply with HIPAA and healthcare security standards.

Key Responsibilities

- Design, build, fine-tune, and deploy machine learning and AI models tailored for production environments.
- Adapt and optimize open-source LLMs and develop AI agents and agentic workflows.
- Develop and maintain data pipelines supporting AI and ML systems, ensuring data quality and transformation.
- Manage the full AI lifecycle, including data preparation, feature engineering, model development, fine-tuning, evaluation, inference, deployment, monitoring, and optimization.
- Analyze and improve existing AI capabilities through iterative feedback loops and model refinement.
- Apply rigorous statistical and mathematical principles to interpret model behavior and performance.
- Work extensively with transformer architectures and modern LLM technologies, making informed decisions on hosted versus self-hosted models.
- Evaluate infrastructure needs, including GPU requirements, latency, scalability, and cost-efficiency.
- Utilize cloud AI infrastructure, potentially including AWS Bedrock and related services, to support model deployment and scaling.
- Build production-quality software, APIs, and automations beyond traditional data science boundaries when necessary.
- Collaborate closely with leadership in a highly autonomous setting to architect AI systems designed for significant growth and compliance with healthcare privacy regulations.

Required Qualifications

- Strong foundation in statistics, probability, mathematics, and machine learning theory.
- Proven experience building and deploying production-grade ML/AI systems.
- Hands-on expertise with LLMs, open-source foundation models, and fine-tuning techniques.
- Deep understanding of transformer architectures and AI agent systems.
- Familiarity with reinforcement learning concepts is highly desirable.
- Solid experience designing and managing data pipelines for model training and deployment.
- Proficiency in ML infrastructure, including model inference optimization, GPU infrastructure evaluation, and cloud AI services (preferably AWS).
- Strong programming skills, particularly in Python and relevant AI/ML frameworks, with the ability to write production-quality code.
- Ability to build APIs and automation tools to support AI applications.
- Experience working in environments requiring strict adherence to security, privacy, and HIPAA compliance.

Preferred Qualifications and Benefits

- Demonstrated ability to move quickly from ambiguous problems to working MVPs.
- Experience in healthcare or other highly regulated industries is a plus.
- Strong ownership mindset with a problem-solving attitude and the ability to work independently without extensive supervision.
- Excellent communication skills to explain complex technical concepts to both technical and non-technical stakeholders.
- Comfort with iterative development, feedback incorporation, and rapid shipping cycles.
- Opportunity to work in a highly autonomous, small team environment directly alongside the company founder.
- Full-time, in-person role requiring compliance with strict HIPAA/security protocols, potentially involving work from controlled environments or company-provided machines.

This role is not suited for candidates who only have superficial AI knowledge, rely solely on calling LLM APIs, lack deep ML fundamentals, or cannot deploy models into production. The successful candidate will quickly understand existing AI systems, identify gaps, improve models with real feedback, and contribute directly to production code while making strategic infrastructure recommendations.

If you are passionate about building scalable, compliant AI systems and thrive in a dynamic, high-impact environment, this opportunity offers a unique chance to lead AI innovation in healthcare.