Multimodal AI Engineer

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. 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
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 who are with one- or two-years’ work experience, with a focus on LLM and image multi-modal foundation models.
Excellent programming skills in Python and C/C++.
Proficiency in PyTorch framework and the common NLP libraries (NLTK, spaCy, scikit-learn, etc.)
Experience with state-of-the-art deep learning architectures (ViT/SWIN transformer, GPT, CLIP, etc.).
Experience with model compression and large scale distributed model training (data-parallel and model-parallel) techniques.
An outstanding track record of publications (NeurIPS, CVPR, ICML, AAAI, etc.) and contributions to the machine learning communities (kaggle, Hugging Face, etc.).
Hands-on experience with parameter-efficient tuning (QLoRA) techniques and the LangChain framework is a plus.
Experience with prompt engineering and fine-tuning Llama 2 or PaLM 2 with domain specific data is a plus.
Experience with CUDA programming is a plus.
Experience with imaging processing (opencv2, VTK, ITK, DCMTK, Albumentations) is a plus.
Experience with vector databases (Chroma, Pinecone, Milvus, redis, etc.) is a plus.
Experience with human-in-the-loop, Reinforcement Learning from Human Feedback (RLHF), and continuous online training is a plus.
Knowledge for Stable Diffusion, OpenJourney, or DeepFloyd IF is a plus.
Responsibilities:
Focus on medical applications, scalable to other applications.  
Multi-Model foundation models, AI Engine to take input imaging, radio, clinical notes, vitals, meds etc.  to create predictions medically. 
Large language model, focus on medical field. 
Produce and create models on clinical fields.  Produce documentation for places for improvement. 
Large language model that takes the data you put in for better results for medical. 
Qualifications:
Required 
Master’s degree in related field with three years of work experience
PhD degree in related field
Excellent programming skills in Python and C/C++. 
Proficiency in PyTorch framework and the common NLP libraries (NLTK, spaCy, scikit-learn, etc.) 
Experience with state-of-the-art deep learning architectures (ViT/SWIN transformer, GPT, CLIP, etc.). 
Experience with model compression and large scale distributed model training (data-parallel and model-parallel) techniques. 
An outstanding track record of publications (NeurIPS, CVPR, ICML, AAAI, etc.) and contributions to the machine learning communities (kaggle, Hugging Face, etc.). 
Hands-on experience with parameter-efficient tuning (QLoRA) techniques and the LangChain framework is a plus. 
        Preferred 
PhD graduates who are with one- or two-years' work experience, with a focus on LLM and image multi-modal foundation models; 
Experience with prompt engineering and fine-tuning Llama 2 or PaLM 2 with domain specific data; 
Experience with CUDA programming; 
Experience with imaging processing (opencv2, VTK, ITK, DCMTK, Albumentations); 
Experience with vector databases (Chroma, Pinecone, Milvus, redis, etc.); 
Experience with human-in-the-loop, Reinforcement Learning from Human Feedback (RLHF), and continuous online training; 
Knowledge for Stable Diffusion, OpenJourney, or DeepFloyd IF. 
Benefits
The base salary range for this full-time position is $151,000 - $200,000.
CliniComp's salary ranges are benchmarked and are determined by role and level. The range displayed on each job posting reflects the minimum and maximum target for new hire salaries for the position across all US locations and could be higher or lower based on a multitude of factors, including job-related skills, experience, location, and relevant education or training.
100% covered Medical and Dental coverage for you & your family depending on the insurance chosen.
Generous 401(k) plan and contribution
Events and weekly lunches
Engaging wellness activities
Corporate Social Responsibility Program
So many more to list…
CCI complies with the Americans with Disabilities Act and considers reasonable accommodation measures that may be necessary for eligible applicants/employees to perform primary responsibilities. EEO/AA/M/F/Veteran/Disabled.