Machine Learning Engineer (Healthcare AI)

Location: Remote within Boston, Massachusetts; New York; New Jersey; New Hampshire; or Rhode Island, United States. Must be a US citizen, Green Card holder, or currently in the US with a valid H1B visa.

Company

hirify.global builds AI technology for healthcare focused on improving patient outcomes and health equity.

What you will do

• Develop, train, fine-tune, evaluate, and deploy large language and machine learning models using large-scale healthcare datasets.
• Clean, transform, normalize, and curate structured and unstructured data, including feature engineering, outlier detection, and missing-value handling.
• Build and maintain scalable data and ML pipelines for training, inference, and production deployment.
• Manage training environments, GPU instances, hyperparameters, and memory optimization for large language models.
• Integrate models into production systems while maintaining healthcare data privacy and security compliance.
• Collaborate with data scientists, clinicians, and software engineers, and document models, pipelines, and experiments.

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.
• Proficiency in Python and ML libraries including TensorFlow, PyTorch, Scikit-learn, Pandas, and NumPy.
• Experience with large-scale data preprocessing, LLM training and fine-tuning, distributed training, and GPU/TPU optimization.
• Experience with PyTorch Distributed, DeepSpeed, Ray, or Hugging Face Accelerate, plus familiarity with MLOps practices and tools.
• US work authorization as a citizen, Green Card holder, or valid H1B visa holder is required.

Nice to have

• Experience working with healthcare data.
• Experience with GCP, AWS, Spark, and distributed computing frameworks.

Culture & Benefits

• Competitive salary and benefits package.
• Flexible working arrangements with remote or hybrid options.
• Work on AI technology designed to impact patient outcomes and health equity.
• Continuous learning with access to current AI and healthcare tools and advancements.