Senior ML Generative AI Scientist

Passionate about precision medicine and advancing the healthcare industry?

Recent advancements in underlying technology have finally made it possible for AI to impact clinical care in a meaningful way. Tempus' proprietary platform connects an entire ecosystem of real-world evidence to deliver real-time, actionable insights to physicians, providing critical information about the right treatments for the right patients, at the right time.

We are seeking a highly skilled and innovative Senior ML Generative AI Scientist to join our research and development team. As a Senior ML Generative AI Scientist, you will play a crucial role in designing, developing, and implementing cutting-edge generative artificial intelligence models and algorithms specifically tailored for healthcare applications. Your work will contribute to improving patient care, optimizing clinical workflows, and advancing medical research. This position offers an exciting opportunity to leverage the power of generative AI to revolutionize healthcare and make a significant impact on people's lives.

What you'll do:

• Responsible for developing innovative and practical Generative AI applications for Tempus product line using Tempus’ large multi-modal datasets spanning Clinical data, Imaging data, and Molecular data.
• Collaborate cross-functionally with other teams to understand needs and requirements, ensuring the Foundation Model/LLMs align with strategic company objectives of embedding AI into our product portfolio.
• Explore and evaluate new technologies and methodologies to enhance the technical capabilities of our team, ensuring that we remain at the cutting edge of ML innovation. Conduct research and exploration of generative AI techniques, with a specific emphasis on healthcare applications. 
• Contribute to thought leadership and continue to develop up-to-date knowledge of Generative AI trends and apply this expertise. Establish and drive research opportunities and collaboration with partner organizations. 
• Design and implement generative AI models tailored for healthcare scenarios. Develop appropriate evaluation metrics that align with healthcare outcomes and clinical requirements, and optimize models to achieve established performance criteria. 
• Stay actively involved in the healthcare and AI research communities. Attend relevant conferences, workshops, and seminars. Publish research findings in reputable scientific venues. Collaborate with academic and industry partners to advance the state-of-the-art in generative AI for healthcare.

Required Qualifications:

• PostDoc or Ph.D. in Computer Science, Artificial Intelligence, Computational Biology, or a related field. A strong academic background with a focus on generative AI and healthcare applications is highly preferred.
• 5+ years of industry experience or equivalent.
• Prior experience in pre-training and fine-tuning of large language models
• Strong collaboration and communication abilities.
• Strong track record in publications, patents, and/or launched products in this space
• Solid knowledge of machine learning concepts, including deep learning, optimization algorithms, regularization techniques, transformer architecture. Extensive experience in developing and implementing generative AI models, such as GANs, VAEs, and related architectures.
• Experience in applying Large Language Models to practical industry challenges, including strong intuition on where to apply generative AI models versus more traditional models
• Proficiency in programming languages commonly used in AI research, such as Python and TensorFlow/PyTorch. Experience with healthcare-specific frameworks (e.g., DICOM, FHIR) is a plus.

Preferred / Nice-to-have Qualifications:

• Experience in temporal modeling, and multi-modal time series data
• Experience in a late-stage startup environment
• Experience building and bringing to practical use, knowledge graphs and graph-based machine learning models
• Expertise with embeddings, multimodal fusion
• Experience working with sensitive healthcare data, medical imaging modalities, clinical workflows, and healthcare terminology. Familiarity with electronic health records (EHRs), and medical imaging formats (e.g., DICOM), molecular data is advantageous.

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