Description
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 Staff ML Generative AI Scientist to join our research and development team. As a Staff 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; for example, build LLM-based generative AI agents that solve impactful business challenges at Tempus across all our business units - touching on clinical notes, electronic health records, medical imaging, sequencing data, etc.
- Collaborate cross-functionally with other teams to understand needs and requirements, ensuring the Foundation Model/LLMs aligns with strategic company objectives of embedding AI into our product portfolio; bring Large Language Models to demonstrable business impact across all of Tempus’ business areas.
- Healthcare-focused research, innovation and product development: Continuously 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. Stay up to date with the latest advancements in the field and adapt them to address healthcare-specific challenges, such as medical image generation, drug discovery, patient data analysis, or personalized medicine.
- Contribute into 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.
- Model Design and Development: Design and implement generative AI models tailored for healthcare scenarios. Develop models that can generate realistic and high-quality medical images, synthetic patient data, or generate text-based medical reports
- Experimentation and Evaluation: Design and execute experiments to evaluate the performance and effectiveness of generative AI models in healthcare settings. Develop appropriate evaluation metrics that align with healthcare outcomes and clinical requirements. Collaborate with domain experts, clinicians, and researchers to validate and refine the models' outputs.
- Collaborative Teamwork: Collaborate closely with cross-functional teams, including healthcare professionals, data scientists, software engineers, and product managers. Leverage their expertise to guide your research, gain insights into real-world healthcare challenges, and translate your findings into practical solutions.
- Prototyping and Implementation: Develop prototypes and proof-of-concept implementations of generative AI solutions for healthcare. Work closely with engineering teams to integrate your models into healthcare platforms, systems, or applications. Ensure scalability, efficiency, and robustness of the deployed solutions.
- Regulatory Compliance and Ethical Considerations: Stay up to date with healthcare regulations, privacy laws, and ethical considerations relevant to the development and deployment of AI in healthcare. Ensure compliance with HIPAA, GDPR, and other applicable standards throughout the development process.
- Industry Engagement: 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:
- Ph.D. or Master's degree in Computer Science, Artificial Intelligence, Biomedical Engineering, 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.
- Strong collaboration and communication abilities.
- Strong track record in publications, patents, and/or launched products in this space
- Experience bringing in thought leadership to the product and team. Bias-to-action, appetite for high-risk high-gain projects with a pragmatic approach to rapid and measurable progress.
- 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 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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