Staff Machine Learning Engineer

About Fullscript

We're an industry-leading health technology company on a mission to help people get better. More than 125,000 practitioners use Fullscript for clinical insights, lab interpretations, patient analytics, education, and access to high-quality supplements. Over 10 million patients rely on Fullscript to stay connected to their care plans.

The Role

We're hiring a Staff Machine Learning Engineer to join our AI team and help shape the next generation of Fullscript's AI-powered experiences. You'll work on building innovative AI capabilities that help clinicians provide better services and help patients improve their health.

This is a senior individual contributor role for someone who can go beyond implementation. In addition to building high-quality systems, you'll help define technical direction, guide architecture decisions, and identify where AI can create meaningful value in clinical workflows. You'll work with a high degree of autonomy and partner closely with engineering, product, analytics, and medical stakeholders.

What you'll do

• Lead the design, development, and deployment of production, multi-turn LLM-powered features, including summarization tools and clinician-facing conversational agents that support follow-up questions and reasoning over clinical context
• Own backend services in Python that integrate LLM agents with Fullscript's platform and support reliable production use
• Help define technical direction for prompting, grounding, safety, and orchestration strategies used across clinical AI workflows
• Establish and improve evaluation approaches for LLM outputs, including accuracy, hallucinations, edge cases, and overall feature quality
• Shape engineering patterns for model-related workflows, including testing, CI/CD, observability, and version control
• Partner with medical, product, and engineering teams to identify high-value opportunities for AI and turn them into practical, scalable product capabilities
• Work cross-functionally with engineering, analytics, and medical SMEs to refine requirements and ensure data and system design support clinical use cases
• Provide technical leadership across projects by creating clarity in ambiguous problem spaces, guiding tradeoff decisions, and raising the quality bar for the team
• Stay current with the latest LLM research and emerging AI technologies

What you bring to the table

• 6+ years of experience building and implementing machine learning applications in production, including meaningful experience with LLM-powered agents, conversational experiences, or agent-based workflows
• A track record of owning complex technical problems end to end and shaping implementation beyond your immediate code contributions
• Experience designing and deploying AI systems that answer open-ended questions, support follow-up interactions, and operate reliably in production
• Strong experience with LLM application frameworks and tooling, such as LangChain, LangGraph, or similar orchestration and RAG frameworks
• Familiarity with evaluation and monitoring frameworks for LLM outputs, conversational quality, and system reliability
• Knowledge of MCP, agent orchestration patterns, or related approaches for building multi-step AI systems
• Strong proficiency in Python and SQL
• Experience making sound technical decisions around quality, safety, maintainability, and scalability in production AI systems
• Strong communication and collaboration skills

Bonus if you have

• Experience defining technical direction for AI or machine learning systems across multiple projects or teams
• Experience building clinician-facing, healthcare-adjacent, or other high-trust AI experiences
• Experience with recommendation systems, personalization, or other applied ML systems beyond LLMs
• Experience with modern retrieval, grounding, or evaluation patterns for LLM applications
• Experience working closely with domain experts to build systems in complex or highly contextual problem spaces

What we can offer you

• Flexible PTO & competitive pay
• RRSP match & stock options
• Customizable benefits
• Fullscript discounts
• Continuous learning — training budget + company-wide initiatives
• Wherever You Work Well — hybrid and remote flexibility