Semantic Health is on a mission to improve care delivery and operational inefficiencies by transforming the use of unstructured data in healthcare's revenue cycle. Our machine learning powered medical coding and auditing platform uses cutting edge deep learning to streamline manual and error-prone medical coding and auditing processes in health organizations. We help health organizations improve data quality, optimize reimbursements, and enable real-time access to actionable data for use across the health system.

At Semantic Health, we combine the clinical and business expertise of doctors and successful entrepreneurs, with the technical skillset of top ML researchers. We are backed by leading institutional investors who have driven companies our size to multi-billion dollar valuations.

We’re seeking motivated and driven remote ML engineers to work on our core product and platform. We value product-focused engineers that thrive by wrangling complex problems into simple solutions.

As a Machine Learning Engineer you will:

• Design, develop, test, and maintain the data processing and ETL pipelines from multiple, disparate structured/unstructured data sources (e.g. HL7 interfaces, medical ontologies, human/crowdsourced inputs)
• Design and implement and maintain the core data models and databases in a scalable and fault-tolerant manner, and build performant interfaces to this data
• Design and build large-scale, cloud or on-premise machine learning pipeline (processing, training, inference, monitoring) in a replicable, well-documented, scalable, and highly performant manner
• Design and build large-scale, cloud or on-premise machine learning platform components (feature store, hosting services, online/offline experiment services, deployment services) in a replicable, well-documented, scalable, and highly performant manner
• Develop and implement novel data-acquisition and labeling systems (e.g. active learning, crowdsourcing)
• Participate in your team’s business hours on-call rotation, triaging and addressing production issues as they arise.

We are looking for people who have:

• Experience designing, implementing, and maintaining data processing and ETL pipelines on multiple, disparate sources of data, preferably with both big data and small data
• Experience designing, implementing, testing, and maintaining machine learning pipelines and platforms
• Experience architecting, writing, optimizing, & debugging software applications, in modern Python stacks with a focus on building scalable ML
• Excitement about learning how to build and support machine learning pipelines that scale not just computationally, but in ways that are flexible, iterative, and geared for collaboration

Bonus points if you have experience with:

• Industry or academic experience working on various ML problems (especially NLP)