Senior Healthcare ML Engineer: LLM Training & Deployment

C the Signs; Boston (MA); Hybrid; USD 140,000 - 210,000; Full time

Benefits offered by this job

Competitive salary

Flexible work arrangements

Life-changing AI technology

Continuous learning

Job summary

C the Signs is seeking a Machine Learning Engineer to lead end-to-end development and deployment of large language and ML models, with a focus on preprocessing, training, and fine-tuning using healthcare data. You will work with clinicians and engineers to productionize scalable ML pipelines while ensuring data privacy and security.

The role requires 5+ years in ML engineering, strong Python skills, and experience with LLMs, distributed training, and cloud-based workflows.

Qualifications

• 5+ years of experience in Machine Learning Engineering or a similar role.
• Experience with large-scale data preprocessing and LLM training.
• Experience with distributed training (PyTorch Distributed, DeepSpeed, Ray).
• Experience with healthcare data is highly desirable.
• Strong Python and ML library proficiency.

Responsibilities

• Preprocess large healthcare datasets for ML development.
• Design, train, and fine-tune LLMs on clinical data.
• Set up GPU instances and training pipelines.
• Evaluate models with appropriate metrics and optimize.
• Develop robust data and ML pipelines for training, inference, deployment.
• Collaborate with clinicians and engineers to deploy in production.
• Document models and experiments.

Skills

Python

Large Language Models

ML Algorithms

Data Preprocessing

Model Training

Hyperparameter Tuning

Collaboration

Communication

Education

Bachelor’s or Master’s in CS/ML/AI

Tools

TensorFlow

PyTorch

Scikit-learn

Pandas

NumPy

Hugging Face Accelerate

Spark

Job description

C the Signs is seeking a Machine Learning Engineer to lead end-to-end development and deployment of large language and ML models, with a focus on preprocessing, training, and fine-tuning using healthcare data. You will work with clinicians and engineers to productionize scalable ML pipelines while ensuring data privacy and security.

The role requires 5+ years in ML engineering, strong Python skills, and experience with LLMs, distributed training, and cloud-based workflows.