Senior Machine Learning Engineer, Applied AI

SimplePractice•Santa Monica, CA

About The Position

At SimplePractice, we are improving access to quality care by equipping health and wellness clinicians with all the tools they need to thrive in private practice. More than 250,000 providers trust SimplePractice to build their business through our industry-leading software with powerful tools that simplify every part of practice management. From admin work to clinical care, our suite of innovative solutions work together to reduce administrative burden—empowering solo and small group practitioners to thrive alongside their clients. Recognized by MedTech Breakthrough as the Best Practice Management Solution Provider in 2024 and the Digital Health Awards in 2023, SimplePractice is proud to pave the future of health tech. The Role Our team is dedicated to empowering clinicians through data-driven innovations. We combine rigorous data science with practical engineering to build systems that make daily workflows more efficient, insightful, and intuitive. If you love tackling challenging problems and turning data into meaningful outcomes, you’ll find a welcoming and dynamic environment here. As a Senior Machine Learning Engineer, Applied AI , you’ll be at the forefront of building product features that help clinicians work effectively and efficiently, providing quality care to patients. You’ll be designing experiments, building robust models, tuning prompts, implementing LLM evals, and driving projects from idea to prototype to production with product, engineering and devOps teams. You’ll also play an important role in the roadmapping exercises of our ML platform. We value mentorship, open communication, and pushing the boundaries of what AI can do in a real-world healthcare context. Whether you’re fine-tuning a model, presenting insights to stakeholders, or brainstorming new product features, your work will have a direct and meaningful impact.

Requirements

BS or above in Computer Science, Statistics or a related technical field
5+ years of experience in Machine Learning, with proven track record of bringing ideas to life, from prototype to productized features
Strong proficiency in Python and hands-on with advanced data analysis tools
Strong skills in data engineering and self-sufficient in data pipelines for the AI workflow
Experience with AWS (or other cloud platforms) for model deployment
Comfortable designing and evaluating LLM-driven workflows
Familiarity with retrieval pipelines and vector databases
Problem-oriented mindset with strong cross-disciplinary thinking and a bias toward simplicity and clarity in solving problems
Comfort working with remote teams, using GitHub, Slack, Notion, and Zoom
Proficiency in English with strong communication and collaboration skill

Nice To Haves

Experience with RAG architecture and context/state management for LLMs
Familiarity with LLM eval tools and human-in-the-loop evaluation process
Experience with Outerbounds or similar ML orchestration platforms
Experience with Argo Flows for CI/CD
Experience with prompt management tool like Langfuse
Familiarity with Kubernetes for container orchestration
Background in healthcare, clinical workflows, or regulated domains

Responsibilities

AI Prototyping and Development Develop AI workflows, customize data pipelines, tune models, and engineer prompts to bring idea to prototype
Work with subject matter experts to set up evaluation for AI workflows, ensuring rigor, quality and safety of content output
Work with eng partners to integrate AI workflows into production
Build and configure AI performance monitoring with proper reporting and alerts
Optimize and maintain AI workflows for performance, reliability, and long-term scalability
Research Start with the Job-to-be-done, dive deep into the domain and understand the problem from user perspective
Decompose problems into conquerable pieces. Design solutions to address each with cross-disciplinary thinking and big picture in mind.
Conduct exploratory data analysis to answer key questions and test assumptions.
Design experiments and build prototypes for proof-of-concept.
Build artifacts to illustrate the findings with rigor and how they inform the roadmap and decisions
Cross-Functional Collaboration Provide AI expert advice to product and eng partners in shaping product roadmap
Partner closely with software eng, product, data eng, ML platform teams to scope and plan in execution
Communicate timeline, milestones, findings w/ internal stakeholders
Mentor & Advocate Best Practices Guide less experienced team members, sharing knowledge on LLM workflows and AI/ML model lifecycle
Champion a culture of experimentation, continuous learning, and proactive problem-solving
Drive Innovation Stay current with emerging ML tools and technologies, integrating new techniques that elevate our product capabilities
Look for creative ways to leverage data to make clinicians’ lives easier, more efficient, and more effective