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

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