GeneDx
Senior Machine Learning Engineer
GeneDx, Gaithersburg, Maryland, us, 20883
GeneDx is seeking an exceptional Machine Learning Engineer to spearhead the development of our groundbreaking automated genetic test writing service. In this role, you will leverage your expertise in Large Language Models (LLMs), machine learning, and product development to create a generative AI system for automating genetic test report writing. This system will integrate test results and our internal knowledge base of structured genetic information to generate professional, accurate, and easily understandable results.
Responsibilities:
Architect and develop a state-of-the-art ML pipeline or agent-based system that utilizes LLMs and context-aware tools to generate genetic test reports.Research, evaluate, and implement various LLMs and ML techniques to optimize the system's performance and output quality.Collaborate closely with the clinical staff to incorporate domain-specific knowledge and ensure the accuracy and relevance of the generated reports.Develop and maintain data preprocessing, feature engineering, and model training pipelines.Implement advanced NLP techniques such as named entity recognition, sentiment analysis, and text summarization to enhance report generation.Continuously monitor, analyze, and improve the system's performance based on user feedback and advancements in ML and LLM technologies.Contribute to the development of a robust, scalable, and secure ML infrastructure.Other duties may be assigned.
Requirements:
Advanced degree in Computer Science, Machine Learning, or a related field.Extensive experience in developing and deploying ML/AI-based systems.Strong Python skills.Strong expertise in natural language processing (NLP) and large language models (LLMs).Experience with LLM libraries such as LangChain, Llama Index, Instructor, DsPy, Outlines, or Instructor.Experience with Vector Databases such as Chroma, Weaviate, PineCone.Proficiency in deep learning frameworks such as TensorFlow, PyTorch, Keras, HuggingFace.Experience with fine-tuning LLMs is a plus.Experience with Kubernetes.Experience with model serving technologies a plus, e.g., KServe, Ray, vLLM, SkyPilot, etc.Experience with ML experiment tracking, CometML, Weights and Biases.Excellent problem-solving, analytical, and communication skills.Ability to work collaboratively in a fast-paced, innovative environment.Knowledge of genetics or experience in the healthcare domain is a plus.
Business Value Add:
Automate the report writing activity which is currently plagued by the tedious work of template selection and minor edits by our highly trained clinical staff.Enable our clinical team to allocate resources more efficiently and focus on high-value tasks such as variant interpretation and difficult cases.Develop a platform for other generative AI applications, such as automated claim denial rebuttals and literature analysis.Establish our company as a technology leader in the genetic testing industry in the use of AI/ML attracting top talent, investors, and partnerships.
Pay Transparency, Budgeted Range:
$153,000—$191,300 USD
Benefits include:
Paid Time Off (PTO)Health, Dental, Vision and Life insurance401k Retirement Savings PlanEmployee DiscountsVoluntary benefits
GeneDx is an Equal Opportunity Employer.All privacy policy information can be found here.
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Responsibilities:
Architect and develop a state-of-the-art ML pipeline or agent-based system that utilizes LLMs and context-aware tools to generate genetic test reports.Research, evaluate, and implement various LLMs and ML techniques to optimize the system's performance and output quality.Collaborate closely with the clinical staff to incorporate domain-specific knowledge and ensure the accuracy and relevance of the generated reports.Develop and maintain data preprocessing, feature engineering, and model training pipelines.Implement advanced NLP techniques such as named entity recognition, sentiment analysis, and text summarization to enhance report generation.Continuously monitor, analyze, and improve the system's performance based on user feedback and advancements in ML and LLM technologies.Contribute to the development of a robust, scalable, and secure ML infrastructure.Other duties may be assigned.
Requirements:
Advanced degree in Computer Science, Machine Learning, or a related field.Extensive experience in developing and deploying ML/AI-based systems.Strong Python skills.Strong expertise in natural language processing (NLP) and large language models (LLMs).Experience with LLM libraries such as LangChain, Llama Index, Instructor, DsPy, Outlines, or Instructor.Experience with Vector Databases such as Chroma, Weaviate, PineCone.Proficiency in deep learning frameworks such as TensorFlow, PyTorch, Keras, HuggingFace.Experience with fine-tuning LLMs is a plus.Experience with Kubernetes.Experience with model serving technologies a plus, e.g., KServe, Ray, vLLM, SkyPilot, etc.Experience with ML experiment tracking, CometML, Weights and Biases.Excellent problem-solving, analytical, and communication skills.Ability to work collaboratively in a fast-paced, innovative environment.Knowledge of genetics or experience in the healthcare domain is a plus.
Business Value Add:
Automate the report writing activity which is currently plagued by the tedious work of template selection and minor edits by our highly trained clinical staff.Enable our clinical team to allocate resources more efficiently and focus on high-value tasks such as variant interpretation and difficult cases.Develop a platform for other generative AI applications, such as automated claim denial rebuttals and literature analysis.Establish our company as a technology leader in the genetic testing industry in the use of AI/ML attracting top talent, investors, and partnerships.
Pay Transparency, Budgeted Range:
$153,000—$191,300 USD
Benefits include:
Paid Time Off (PTO)Health, Dental, Vision and Life insurance401k Retirement Savings PlanEmployee DiscountsVoluntary benefits
GeneDx is an Equal Opportunity Employer.All privacy policy information can be found here.
#J-18808-Ljbffr