SEOsatu
Multimodal AI Engineer San Diego CliniComp
SEOsatu, San Diego, California, United States, 92189
You’ll tackle complex challenges in computer science, focusing on data curation, training advanced AI models, and generating impactful insights within our healthcare-focused environment.
Requirements
As our newly appointed Multimodal AI Engineer, you’ll lead pioneering initiatives in healthcare AI, focusing on LLM and image multi-modal foundation models. You’ll drive cutting-edge research, harnessing state-of-the-art AI technologies to create predictive models for medical applications. Collaborating within our dynamic team, your role involves pushing the boundaries of AI machine learning, refining multimodal foundation models, and contributing to real-world healthcare solutions.
Responsibilities
Focus on medical applications, scalable to other applications.
Develop multi-model foundation models, integrating input from imaging, radiology, clinical notes, vitals, and medications to create medical predictions.
Work on large language models with a focus on the medical field.
Produce and document models in clinical fields, identifying areas for improvement.
Qualifications
Multimodal AI Expertise:
Proficiency in designing, implementing, and fine-tuning models that handle diverse data types (LLM, imaging, clinical notes, etc.) using various AI architectures (transformers, GPT, etc.).
Data Curation and Preparation:
Experience and knowledge in best practices for preparing and curating multimodal datasets for training AI models, particularly in the healthcare domain.
Programming Skills:
Excellent proficiency in Python and C/C++ for implementing and optimizing AI models and algorithms.
Deep Learning Frameworks:
Strong command over PyTorch and familiarity with NLP libraries (NLTK, spaCy, scikit-learn) for building and training deep learning models.
Model Compression and Scalability:
Expertise in model compression techniques and large-scale distributed model training, ensuring efficiency and scalability.
Research and Publication Record:
Demonstrated track record of publications in top-tier conferences (NeurIPS, CVPR, ICML, AAAI, etc.) and active contributions to machine learning communities (Kaggle, Hugging Face).
Innovation and Adaptability:
Ability to address challenging problems in computer science, drive innovation, and adapt to cutting-edge AI techniques for healthcare applications.
Domain-Specific AI Application:
Experience in producing and creating AI models specifically tailored for clinical fields, demonstrating a deep understanding of medical applications.
Technical Proficiency:
Experience with CUDA programming, imaging processing libraries (opencv2, VTK, ITK, DCMTK, Albumentations), and vector databases (Chroma, Pinecone, Milvus, Redis) for handling medical data and large-scale processing.
Advanced AI Techniques:
Knowledge or experience in advanced AI concepts like parameter-efficient tuning, domain-specific model fine-tuning, human-in-the-loop learning, and reinforcement learning from human feedback is advantageous. PhD graduates with one to two years of work experience focusing on LLM and image multi-modal foundation models are preferred.
The base salary range for this full-time position is $151,000 – $200,000.
Benefits
100% covered medical and dental coverage option for you and your family.
Generous 401(k) plan and contribution.
Events and biweekly lunches.
Engaging wellness activities including an onsite nutritionist and personal trainer-led group fitness.
Corporate Social Responsibility Program.
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Requirements
As our newly appointed Multimodal AI Engineer, you’ll lead pioneering initiatives in healthcare AI, focusing on LLM and image multi-modal foundation models. You’ll drive cutting-edge research, harnessing state-of-the-art AI technologies to create predictive models for medical applications. Collaborating within our dynamic team, your role involves pushing the boundaries of AI machine learning, refining multimodal foundation models, and contributing to real-world healthcare solutions.
Responsibilities
Focus on medical applications, scalable to other applications.
Develop multi-model foundation models, integrating input from imaging, radiology, clinical notes, vitals, and medications to create medical predictions.
Work on large language models with a focus on the medical field.
Produce and document models in clinical fields, identifying areas for improvement.
Qualifications
Multimodal AI Expertise:
Proficiency in designing, implementing, and fine-tuning models that handle diverse data types (LLM, imaging, clinical notes, etc.) using various AI architectures (transformers, GPT, etc.).
Data Curation and Preparation:
Experience and knowledge in best practices for preparing and curating multimodal datasets for training AI models, particularly in the healthcare domain.
Programming Skills:
Excellent proficiency in Python and C/C++ for implementing and optimizing AI models and algorithms.
Deep Learning Frameworks:
Strong command over PyTorch and familiarity with NLP libraries (NLTK, spaCy, scikit-learn) for building and training deep learning models.
Model Compression and Scalability:
Expertise in model compression techniques and large-scale distributed model training, ensuring efficiency and scalability.
Research and Publication Record:
Demonstrated track record of publications in top-tier conferences (NeurIPS, CVPR, ICML, AAAI, etc.) and active contributions to machine learning communities (Kaggle, Hugging Face).
Innovation and Adaptability:
Ability to address challenging problems in computer science, drive innovation, and adapt to cutting-edge AI techniques for healthcare applications.
Domain-Specific AI Application:
Experience in producing and creating AI models specifically tailored for clinical fields, demonstrating a deep understanding of medical applications.
Technical Proficiency:
Experience with CUDA programming, imaging processing libraries (opencv2, VTK, ITK, DCMTK, Albumentations), and vector databases (Chroma, Pinecone, Milvus, Redis) for handling medical data and large-scale processing.
Advanced AI Techniques:
Knowledge or experience in advanced AI concepts like parameter-efficient tuning, domain-specific model fine-tuning, human-in-the-loop learning, and reinforcement learning from human feedback is advantageous. PhD graduates with one to two years of work experience focusing on LLM and image multi-modal foundation models are preferred.
The base salary range for this full-time position is $151,000 – $200,000.
Benefits
100% covered medical and dental coverage option for you and your family.
Generous 401(k) plan and contribution.
Events and biweekly lunches.
Engaging wellness activities including an onsite nutritionist and personal trainer-led group fitness.
Corporate Social Responsibility Program.
#J-18808-Ljbffr