[Remote] AI Engineer (API servers and language modeling)
Salário Estimado
R$ 12.500,00 - R$ 16.667,00
Descrição da Vaga
Note: The job is a remote job and is open to candidates in USA.
Cinnamon Inc. is a forward-thinking healthcare technology company dedicated to improving patient advocacy through secure and reliable predictive intelligence.
They are seeking an AI Engineer to build consumer-facing and internal products around language models in the healthcare and patient advocacy space, focusing on high availability and efficiency while incorporating specific needs of ML workflows.
Responsibilities • Incorporate models from our research team into web APIs that are accessed by our consumer product, specifically:
Company Overview • Cinnamon has changed the business model for patient assistance technology by moving the cost from the healthcare provider to pharmaceutical companies and their affiliates.
It was founded in 2023, and is headquartered in Oakland, California, USA, with a workforce of 11-50 employees.
Its website is https://cinnamon.health.
Requisitos
- 5+ years experience working on server side web applications
- Demonstrated experience working with language models
- Ability and willingness to 'wear many hats' in a fast moving startup environment
- Attention to detail with a security/safety mindset and a willingness to say 'no' when appropriate
- Healthcare is a highly regulated industry
- AI is an emerging technology that poses both unique security issues and unique ethical concerns
- Patient outcomes and regulatory compliance must be prioritized over other business concerns
- 'If you see something, say something.'
- Experience owning production systems end-to-end, including on-call support, incident response, or reliability improvements
Responsabilidades
- Incorporate models from our research team into web APIs that are accessed by our consumer product, specifically:
- Zero-shot relevance classification of health records given information about a drug and medical condition
- Medical question answering grounded by a patient’s medical history
- Implement monitoring systems to detect and flag probable errors, model drift, and adversarial inputs
- Implement data capture systems to collect future training data from our production systems
- Collaborate with the research team on the design of these systems, and to make the data available to researchers and applied scientists
- Collaborate with the application team to develop and support new customer-facing features that require AI interaction
- All while strictly adhering to HIPAA and other relevant regulations
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