Build and scale the data and analytics platform behind clinical AI products, developing secure pipelines, performance monitoring, and self-serve tools that turn complex clinical data into actionable insights.
Bayesianhealth jobs
Patient monitoring and clinical guidance platform.
12 open roles at Bayesianhealth
Own full-cycle recruiting across technical and non-technical teams, with a focus on engineering, data science, and AI/ML, while improving sourcing, candidate experience, recruiting operations, and hiring speed.
Own product marketing across sales enablement, module launches, and customer expansion, translating clinical outcomes and technical capabilities into credible messaging that advances health system deals and renewals.
Own software design quality, post-market complaint investigations, and regulatory processes for an FDA-cleared AI clinical platform, partnering across engineering, data science, clinical, and implementation teams.
Investigate sepsis alert cases by reviewing clinical records, assessing product behavior, and communicating evidence-based findings; improve review processes, manage escalations, and support complaint handling for a regulated clinical.
Build and deploy integrations that connect the Bayesian platform with complex EHR systems, bringing real-time clinical insights into care workflows while troubleshooting issues and improving integration tooling.
Build and operate production data integrations between complex EHR systems and a clinical platform, turning fragmented health data into real-time insights while supporting testing, deployment, and go-live.
Build and operate reliable, cost-optimized AWS infrastructure for a healthcare platform, developing CI/CD pipelines and secure AI Ops capabilities while enabling engineering and data science teams to ship faster.
Set the machine learning organizationβs technical vision, lead and grow a high-performing team, and contribute hands-on to production models and infrastructure that support high-impact healthcare products.
Own end-to-end development and real-world effectiveness of clinical machine learning models, from prototyping and production deployment to evaluating performance and aligning with clinical, engineering, and product teams.
Own the end-to-end effectiveness of clinical machine learning models, from prototyping and productionization to deployment and MLOps, improving real-time predictions in a live clinical AI product.
Lead enterprise revenue growth across a territory by developing sales and account strategies, building executive relationships within health systems, and coaching a team to drive adoption of healthcare technology.
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