Senior Machine Learning Engineer
New
C
C the SignsHealthcare AI
Boston, Massachusetts, United States. New York, United States. New Jersey, United States. New Hampshire, United States. Rhode Island, United StatesFull-TimeSenior
Salary not disclosed
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Job Details
- Experience
- 5+ years
- Required Skills
- AWSPythonGCPNumpyPyTorchPandasSparkTensorflowscikit-learnMLOps
Requirements
- Bachelor's or Master's degree in Computer Science, Machine Learning, Artificial Intelligence, or a related quantitative field.
- 5+ years of experience in Machine Learning Engineering or a similar role.
- Proven experience with large-scale data preprocessing, LLM/model training, and fine-tuning.
- Experience with distributed training (PyTorch Distributed, DeepSpeed, Ray, Hugging Face Accelerate).
- Experience with GPU/TPU optimization, memory management for large language models.
- Proficiency in Python and relevant ML libraries (e.g., TensorFlow, PyTorch, Scikit-learn, Pandas, NumPy).
- Strong understanding of various machine learning algorithms, Large Language Models, and deep learning architectures.
- Experience with cloud platforms (e.g., GCP, AWS) and distributed computing frameworks (e.g., Spark) is a plus.
- Familiarity with MLOps practices and tools.
- Excellent problem-solving and analytical skills.
- Strong communication and collaboration abilities.
- Ability to work independently and as part of a team in a fast-paced environment.
- Experience working with healthcare data is highly desirable.
Responsibilities
- Develop and deploy Large language and machine learning models end-to-end.
- Clean, transform, and prepare large, complex healthcare datasets for machine learning model development, including handling missing values, outlier detection, feature engineering, and data normalization.
- Identify, collect, and curate relevant, industry-specific datasets for model retraining and format data appropriately for LLM and training pipeline.
- Design, train, and fine-tune various LLMs on extensive healthcare data to solve specific clinical or operational problems.
- Set up and manage the training environment, including GPU instances and required software.
- Experiment with and fine-tune hyperparameters such as learning rate, batch size, and training epochs to optimize model performance.
- Integrate structured and unstructured data (multi-modal/multi-input models).
- Evaluate model performance using appropriate metrics, identify areas for improvement, and implement optimization strategies.
- Develop and maintain robust and scalable data and ML pipelines for model training, inference, and deployment.
- Work closely with data scientists, clinicians, and software engineers to understand requirements, integrate models into production systems, and ensure data privacy and security compliance.
- Stay up-to-date with the latest advancements in machine learning and healthcare AI, and explore new technologies and methodologies.
- Maintain clear and comprehensive documentation of models, data pipelines, and experimental results.
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