Multimodal AI Engineer

4 weeks ago

San Diego, California, United States CliniComp Full time

CliniComp is seeking a highly skilled Multimodal AI Engineer to lead pioneering initiatives in healthcare AI, focusing on LLM and image multi-modal foundation models.

You will 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.

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.

The successful candidate will:

  • Focus on medical applications and scale them to other applications.
  • Work with multimodal foundation models/AI Engine to take input imaging, radio, clinical notes, vitals, meds etc. to create medical predictions.
  • Have worked with large language model (LLM) with a focus on the medical field.
  • Produce and create models on clinical fields and produce documentation for places of improvement.

Requirements:

This position is based onsite in San Diego, CA.

Required PhD in Computer Science or Computer Engineering.

2+ years of industry work experience.

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.

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