Hippocratic AI has developed a safety-focused Large Language Model (LLM) for healthcare. The company believes that a safe LLM can dramatically improve healthcare accessibility and health outcomes in the world by bringing deep healthcare expertise to every human. No other technology has the potential to have this level of global impact on health.
Why Join Our Team:
Innovative Mission: We are developing a safe, healthcare-focused large language model (LLM) designed to revolutionize health outcomes on a global scale.
Visionary Leadership: Hippocratic AI was co-founded by CEO Munjal Shah, alongside a group of physicians, hospital administrators, healthcare professionals, and artificial intelligence researchers from leading institutions, including El Camino Health, Johns Hopkins, Stanford, Microsoft, Google, and NVIDIA.
Strategic Investors: We have raised a total of $278 million in funding, backed by top investors such as Andreessen Horowitz, General Catalyst, Kleiner Perkins, NVIDIA’s NVentures, Premji Invest, SV Angel, and six health systems.
World-Class Team: Our team is composed of leading experts in healthcare and artificial intelligence, ensuring our technology is safe, effective, and capable of delivering meaningful improvements to healthcare delivery and outcomes.
We are looking for a Conversation AI Engineer to bend language models to their will. In this role, you will collaborate with engineers and research scientists to enhance the effectiveness and safety of generative AI solutions by designing, testing, and improving prompts that drive clinical safety and patient experience. You will also create automations to test your creations. An ideal candidate is equal parts software engineer and prompt engineer, loves experimentation and tinkering, is extremely thorough and detail oriented, and has a passion for conversation and communication.
Responsibilities:
Help achieve world class performance on several safety-critical LLM applications
Collaborate with product managers to write detailed product specs for how models should behave
Help design agentic model workflows
Use advanced prompting techniques to develop and optimize prompts for language models to improve model performance, clinical safety, and patient experience.
Contribute to automated model testing and evaluation infrastructure
Build automations to create and select the best prompts for any given task
Conduct experiments and analyze outcomes of model outputs to refine and iterate on prompt strategies.
Write and configure prompts to create engaging, patient-oriented conversations
Experience (5+ years) in a technical or semi-technical field, such as engineering, analytics, data science, or product management
Demonstrated product sense and intuition for conversation design
Proficiency writing simple scripts in Python
Familiarity with basic principles of machine learning, conversational AI, and Large Language Models
Experience developing, designing, and/or interacting with non-deterministic AI/ML systems
Experience designing and conducting experiments and statistical analysis, specifically measuring and optimizing the accuracy of model outputs
Experience with written language, communications and/or conversation design
Software engineering experience
Given that the field of prompt engineering is very new, we understand that it’s unlikely that you will have direct experience on your resume. While direct experience in prompt engineering is not expected, we value transferable skills that align with the responsibilities of the position. Ideal candidates might come from various related fields where they have honed relevant skills in problem-solving, analytical thinking, and written communication.
You might be a good fit if you have an engineering background and have or are looking to move into a product role. Perhaps you’re a data scientist who writes on the side. Or maybe you are a conversation designer who dabbled in computational linguistics.
We are looking for high-aptitude adaptive thinkers who are willing to jump in and solve new problems using a variety of technical and non-technical approaches.
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