Applied ML Engineer

Key Responsibilities

• Productionize ML models for real-time clinical applications
• Optimize inference pipelines for low latency and high throughput
• Deploy and scale models using AWS-based infrastructure
• Build automated evaluation and regression testing frameworks for LLM outputs
• Implement monitoring systems for model performance and drift detection
• Collaborate with Backend teams to integrate ML services into APIs and workflows
• Improve model efficiency through quantization, batching, caching, and optimization techniques
• Support specialty-level model evaluation and performance analysis
• Contribute to CI/CD workflows for ML deployment

Required Qualifications

• 3–7+ years of experience in machine learning engineering or applied ML roles
• Strong proficiency in Python and PyTorch (or TensorFlow)
• Experience deploying ML models in production environments
• Familiarity with transformer architectures and large language models
• Experience with model optimization techniques (quantization, distillation, pruning)
• Experience working with cloud infrastructure (AWS preferred)
• Strong software engineering fundamentals and debugging skills

Preferred Qualifications

• Experience with speech recognition systems or NLP pipelines
• Experience with Triton Inference Server or similar deployment frameworks
• Familiarity with healthcare data or clinical documentation workflows
• Experience working in regulated environments (HIPAA, GovCloud, etc.)
• Knowledge of medical coding systems (ICD-10, CPT)