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Truveta is the world’s first health provider led data platform with a vision of Saving Lives with Data. Our mission is to enable researchers to find cures faster, empower every clinician to be an expert, and help families make the most informed decisions about their care. Achieving Truveta’ s ambitious vision requires an incredible team of talented and inspired people with a special combination of health, software and big data experience who share our company values.
Job Responsibility:
Collaborate with researchers and engineers to design, develop, and refine large language models and generative models for various applications
Utilize your expertise in machine learning and natural language processing to develop novel algorithms and methodologies for generative modeling tasks
Implement, train, and fine-tune LLM and GPT-like models on large-scale datasets to ensure optimal performance and accuracy
Stay up to date with the latest research advancements and techniques in the field of language modeling, generative modeling, and machine learning
Deliver the next generation of innovation in trustworthy healthcare
Requirements:
Ph.D. in Computer Science, Electrical Engineering, or a related field, with a focus on machine learning, natural language processing (NLP), Large Language Models (LLMs), multi-modal foundation models, and generative AI
Strong theoretical and practical background in NLP including experience with state-of-the-art architectures
Proficiency in deep learning frameworks (e.g., PyTorch, TensorFlow, etc.) and libraries commonly used in NLP and Generative AI
Solid programming skills in Python and the ability to write clean, efficient, and well-documented code
Excellent problem-solving and troubleshooting abilities, along with a strong analytical mindset and persistence in resolving problems
Strong communication skills and the ability to work effectively in a collaborative research environment
Nice to have:
Experience with distributed parallel training, large-scale multi-modal foundation and generative models
Familiarity with parameter-efficient tuning techniques, Reinforcement Learning from Human Feedback (RLHF), and prompt engineering techniques
Familiarity with training multi-modal foundation models
Familiarity with cloud-based infrastructure and experience deploying large-scale machine learning models in production environments
A track record of publications and contributions to the machine learning and natural language processing communities
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