Hybrid Machine Learning Engineer

at Welldoc

• Job title: Machine Learning Engineer
• Job type: Full Time
• Experience level: Mid level, Senior
• Salary: $100,000 - $140,000 per year
• Degree requirement: Bachelor's Degree
• Tech skills: AWS, Azure, Cloud, Docker, ETL, Google Cloud Platform, Kubernetes, Python, PyTorch
• Location requirements: Hybrid, Columbia, United States

About the role

• Machine Learning Engineer developing and deploying AI solutions at Welldoc for healthcare improvements. Collaborating with cross-functional teams and optimizing machine learning models in a hybrid work environment.

Responsibilities

• Develop, train, and optimize machine learning models using PyTorch and other ML frameworks.
• Deploy and maintain ML models in production environments, ensuring scalability, performance, and reliability.
• Utilize Databricks for data processing, model training, model deployment, and pipeline optimization.
• Deploy Retrieval-Augmented Generation (RAG) pipelines to production for improved AI-driven applications.
• Collaborate with data engineers to design and implement ETL workflows and data pipelines.
• Perform rigorous testing, validation, and monitoring of deployed models.
• Optimize model inference for low latency and high throughput applications.
• Work with stakeholders to translate business problems into ML solutions.
• Stay up to date with the latest advancements in machine learning, deep learning, and AI deployment strategies.

Requirements

• Proficiency in Python and ML frameworks such as PyTorch.
• 3-5 years of experience deploying machine learning models to production.
• Knowledge of MLflow for experiment tracking and model management.
• Strong experience with Databricks for ML development and deployment.
• Hands-on experience with MLOps, CI/CD pipelines, and cloud-based deployment (AWS, Azure, or GCP).
• Solid understanding of data structures, algorithms, and software engineering principles.
• Experience working with large-scale datasets and distributed computing frameworks.
• Experience with deploying Retrieval-Augmented Generation (RAG) pipelines to production.
• Excellent analytical and problem-solving skills.
• Strong communication skills and ability to work in a collaborative team environment.
• Experience deploying models in the healthcare domain (preferred).
• Experience with feature engineering, data preprocessing, and model explainability (preferred).
• Knowledge of containerization (Docker, Kubernetes) and workflow orchestration tools (preferred).

Benefits

• generous PTO
• medical insurance
• dental insurance
• vision care
• life and disability insurance
• retirement benefits
• opportunity to participate in health savings accounts and/or dependent care accounts