Senior Machine Learning Engineer, Artera

• Bachelor’s degree in a STEM field, or equivalent practical experience. Master’s or PhD holders may substitute for years of industry experience
• 5+ years of industry experience in applied machine learning or AI engineering; advanced degrees (Master’s or PhD) may offset years of experience
• Proven experience shipping models into production (not just proof-of-concepts)
• Proficiency in Python or TypeScript; strong SQL skills for working with large-scale data
• Experience with LLMs and NLP frameworks (e.g., TensorFlow, Hugging Face, LangChain)
• Cloud infrastructure experience, ideally AWS
• Understanding of MLOps, including orchestration tools like Airflow or Dagster
• Strong collaboration skills—comfortable working with PMs, designers, and engineers

Desirable

• At Artera, we’re reimagining how patients connect with their care teams through smart, meaningful, and autonomous conversations
• Our Digiorno team (ML team) is partnering with our Engineering foundations team to champion this effort, building AI experiences that do more than respond, they take action
• From scheduling appointments to following up on care plans, our agents handle real patient needs in real time, making healthcare more accessible and efficient for everyone involved
• This role offers the chance to work on the frontier of agentic AI, applying cutting-edge technologies like LLMs and co-pilot frameworks to real-world healthcare use cases. Unlike many companies still laying the groundwork, Artera has the structured data, workflows, and operational maturity to deliver AI that’s not only intelligent but trusted and useful
• As a Senior Machine Learning Engineer on this team, you’ll move fast, collaborate cross-functionally, and bring AI agents from discovery all the way through production, helping to shape the future of patient experience in healthcare
• Build and ship production-ready AI agents that automate key healthcare workflows (e.g., appointment setting, follow-up care, password resets)
• Design and implement workflows and scripts for agentic conversations based on patient flows and real-world data
• Collaborate closely with Engineering Foundations, Product, Design, and Engineering teams to perform discovery and guide the development of use-case-driven agents
• Conduct end-to-end development including data gathering, hypothesis testing, prototyping, demoing, productionizing, and monitoring
• Implement NLP and LLM-powered components for sentiment analysis, real-time conversation evaluation, and behavior optimization
• Design evaluation agents to enhance the quality and coherence of autonomous conversations
• Work within a modern MLOps environment to ensure scalable and reliable deployment of models
• Contribute to analytics and predictive features such as no-show prediction and sentiment dashboards
• Translate complex ML workflows into digestible updates for cross-functional stakeholders
• Contribute to backlog velocity by owning appropriate tickets and delivering high-impact work in a collaborative, fast-paced environment