Cohere HealthRemote
Senior Machine Learning Engineer
United States of America
Full Time
5 hours ago
$146,000 - $180,000 USD
H1B Sponsor Likely
Key skills
Machine LearningDeep LearningTransformer-based ModelsCNN-based ModelsNatural Language ProcessingComputer VisionPyTorchTensorFlowScikit-LearnKerasSpacyHugging FacePythonPandasNumpyAWS S3AWS AthenaAWS EC2AWS LambdaAWS SageMakerAWS CloudWatchAWS BedrockSQLDockerLinuxLangChainLlamaIndexJIRAConfluenceGitAgileAIArtificial IntelligenceMLNLPGenerative AIRAGscikit-learnNumPyAnalyticsAWSEC2LambdaS3CloudWatchAthenaSageMakerBedrockJiraLeadership
About this role
Cohere Health, Inc. is a company focused on improving healthcare outcomes, and they are seeking a Senior Machine Learning Engineer. The role involves designing, deploying, and monitoring machine learning algorithms to extract and predict clinical findings from various data sources, while also providing guidance and mentorship to junior engineers.
Responsibilities:
- Evaluate state-of-the-art deep learning approaches (e.g. transformers) for embedding, retrieval, classification and generative use cases
- Write and maintain reusable codebases to perform data preprocessing, model training, evaluation, and deployment
- Build, deploy and monitor reliable and scalable production machine learning systems
- Work cross-functionally with diverse stakeholders including product managers, clinicians, and technical leadership
- Develop multi-modal document understanding systems for information extraction, incorporating segmentation, ordering, and classification to enrich metadata for improved retrieval and generation workflows
- Provide software and machine learning best practice guidance to junior engineers
- Provide onboarding support and mentorship to interns
Requirements:
- Must have a Master's degree in Computer Science, Artificial Intelligence, Machine Learning, Mathematics or closely-related field
- 3 years of professional experience as a machine learning engineer performing software platform development
- End-to-end machine learning lifecycle within an Agile environment
- Developing and deploying machine learning models for NLP and computer vision, implementing architectures such as Transformer-based models, and CNN-based models
- Frameworks and libraries including: PyTorch, TensorFlow, Scikit-Learn, Keras, Spacy, Hugging Face, and ML applications
- Applying statistical and ML techniques leveraging Python, including Pandas and Numpy
- Cloud computing and data analytics using AWS services (including S3, Athena, EC2, Lambda, SageMaker, CloudWatch, and Bedrock), along with SQL, Docker, and Linux for scalable processing
- Designing, building, and evaluating RAG-based Generative AI applications using LangChain, LlamaIndex or similar while managing workflows with JIRA, Confluence, and Git