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AI Scientist I (Healthcare)

LinkedIn Cambia Health Solutions • Salt Lake City, UT
Not Applicable Posted March 13, 2026 Job link
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Requirements
  • 0-3 years of related work experience
  • Demonstrated knowledge of generative AI, machine learning and data science.
  • Ability to use well-understood techniques and existing patterns to build, analyze, deploy, and maintain models.
  • Effective in time and task management.
  • Able to develop productive working relationships with colleagues and business partners.
  • Strong interest in the healthcare industry.
  • Generative AI:Understanding of foundation models, transformer architectures, and techniques for working with large language models (LLMs).
  • Experience with prompt engineering, fine-tuning approaches, and evaluation methods for generative models.
  • Machine Learning:Strong mathematical foundation and theoretical grasp of the concepts underlying machine learning, optimization, etc. (see below).
  • Demonstrated understanding of how to structure simple machine learning pipelines (e.g, has prepared datasets, trained and tested models end-to-end).
  • Data:Strong foundation in data analysis.
  • Programming:Strong python programming skills.
  • Familiarity with standard data science packages.
  • Familiarity with standard software development best practices.
  • Strong SQL skills a plus.
  • Algorithms:Understanding of standard algorithms and data structures (ex. search and sort) and their analysis.
  • Core Knowledge Details and Examples (meant to be representative, not exhaustive; entry level roles are expected to have hands-on experience training and testing AI models, solid mathematical understanding and computer science fundamentals)
  • Generative AI
  • Large Language Models (LLMs) and their capabilities (e.g, in-context learning, few-shot learning, zero-shot learning)
  • Prompt engineering techniques and best practices
  • Fine-tuning approaches (e.g, full fine-tuning, parameter-efficient methods like LoRA, QLoRA)
  • Retrieval-Augmented Generation (RAG) and knowledge integration
  • Evaluation methods for generative models (e.g, perplexity, BLEU, ROUGE, human evaluation)
  • Alignment techniques (e.g, RLHF, constitutional AI, red-teaming)
  • Multimodal generative models (text-to-image, text-to-video, multimodal understanding)
  • Responsible AI considerations specific to generative models (e.g, bias, hallucinations, safety)
  • Familiarity with Gen AI frameworks and tools (e.g, Hugging Face and LangChain)
  • Machine Learning
  • Classic ML algorithms (e.g, linear and logistic regression, decision and boosted trees, SVM, collaborative filtering, ranking)
  • Approaches (e.g, supervised, semi-supervised, unsupervised, reinforcement learning, regression, classification, time series modeling, transfer learning)
  • Foundational ML concepts such as objective functions, regularization and over fitting
  • Data partitions (train/dev/test) and model development
  • Hyperparameter tuning and grid search
  • Evaluation concepts (metrics, feature importance, etc.)
  • Familiarity with standard python packages (scikit-learn, XGBoost, TensorFlow, PyTorch, etc.)
  • Familiarity with structure of machine learning pipelines
  • Deep Learning (basic understanding expected at all levels)
  • Activation functions
  • Common architectures (CNN, RNN, LSTM, GAN, etc.)
  • Familiarity with specializations (sequence modeling/NLP/computer vision)
  • Linear Algebra
  • Discrete math
  • Calculus
Preferred Skills
  • Strong interest in the healthcare industry.
  • Experience with prompt engineering, fine-tuning approaches, and evaluation methods for generative models.
  • Strong SQL skills a plus.
  • Algorithms:Understanding of standard algorithms and data structures (ex. search and sort) and their analysis.
  • Core Knowledge Details and Examples (meant to be representative, not exhaustive; entry level roles are expected to have hands-on experience training and testing AI models, solid mathematical understanding and computer science fundamentals)
  • Generative AI
  • Large Language Models (LLMs) and their capabilities (e.g, in-context learning, few-shot learning, zero-shot learning)
  • Fine-tuning approaches (e.g, full fine-tuning, parameter-efficient methods like LoRA, QLoRA)
  • Retrieval-Augmented Generation (RAG) and knowledge integration
  • Evaluation methods for generative models (e.g, perplexity, BLEU, ROUGE, human evaluation)
  • Alignment techniques (e.g, RLHF, constitutional AI, red-teaming)
  • Multimodal generative models (text-to-image, text-to-video, multimodal understanding)
  • Responsible AI considerations specific to generative models (e.g, bias, hallucinations, safety)
  • Familiarity with Gen AI frameworks and tools (e.g, Hugging Face and LangChain)
  • Machine Learning
  • Classic ML algorithms (e.g, linear and logistic regression, decision and boosted trees, SVM, collaborative filtering, ranking)
  • Approaches (e.g, supervised, semi-supervised, unsupervised, reinforcement learning, regression, classification, time series modeling, transfer learning)
  • Foundational ML concepts such as objective functions, regularization and over fitting
  • Hyperparameter tuning and grid search
  • Familiarity with standard python packages (scikit-learn, XGBoost, TensorFlow, PyTorch, etc.)
  • Familiarity with structure of machine learning pipelines
  • Deep Learning (basic understanding expected at all levels)
  • Familiarity with specializations (sequence modeling/NLP/computer vision)
Education
  • (Not required) – Bachelor’s degree (masters or PhD preferred) in a strongly quantitative field such as Computer Science, Statistics, Applied Mathematics, Physics, Operations Research, Bioinformatics, or Econometrics
  • (Not required) – Equivalent combination of education and experience
  • (Not required) – Math
  • (Not required) – Linear Algebra
  • (Not required) – Discrete math
  • (Not required) – Probability and Statistics
  • (Not required) – Calculus