Senior Machine Learning / Backend Engineer

About Plenful

Plenful is on a mission to transform healthcare operations from the inside out. Fresh off our $50M Series B and backed by Bessemer Venture Partners, Notable Capital, TQ Ventures, Susa / Kivu Ventures, and other leading investors, we’re building the category-defining AI agentic operating platform that healthcare teams rely on to operate smarter, faster, and more efficiently. Our technology empowers healthcare operators across hospital and health systems, pharmacies, and payors to eliminate manual work, reduce administrative burden, and improve compliance, all while unlocking critical revenue to fund programs for their in-need patient populations.

Built by healthcare operators for healthcare operators, Plenful is driven by a deep understanding of the challenges facing today’s care teams. We’re passionate about equipping healthcare workers with world-class tools that deliver real, measurable impact, and we’re proud to serve leading healthcare organizations across the country. If you’re excited to help shape the future of healthcare, we’d love to meet you. Apply now to join our growing team.

About the role

We are hiring an exceptional Senior Machine Learning Backend Engineer to lead the integration of advanced ML solutions into our platform. At Plenful, we believe the Engineering-Product-Design (EPD) triangle is essential to our product’s success. As a Senior ML Backend Engineer, you will own the end-to-end development of ML-driven features—designing scalable systems, deploying models into production, and ensuring performance, compliance, and reliability in healthcare workflows.

What you’ll do

• Design, build, and maintain backend services and APIs inPython, with a focus on integrating and deploying machine learning models.
• Architect scalable infrastructure onAWS(S3, Lambda, SQS, ECS, CloudWatch) to support ML pipelines and inference at scale.
• Collaborate with product managers, designers, data scientists, and engineers to define and deliver ML-powered product features.
• Deploy, monitor, and optimize ML models in production, ensuring performance, security, and compliance withHIPAAand other healthcare regulations.
• Develop and optimizedata pipelinesfor training, inference, and real-time decision-making.
• Implement best practices forCI / CD, testing (Pytest), and version control (Git) to ensure reliable ML feature deployment.
• Troubleshoot and resolve production issues across ML services, APIs, and infrastructure.
• Document architectural decisions, ML workflows, and compliance / security practices.
• Provide technical leadership—mentoring engineers, reviewing code, and championing backend + ML integration best practices.

What we’re looking for

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