Description:

SuperDial is seeking a Machine Learning Engineer (MLE) to build and deploy AI-driven solutions that transform healthcare operations.
The role involves designing, developing, and deploying production-grade machine learning models to enhance revenue cycle management and healthcare workflows.
Responsibilities include optimizing and scaling ML inference pipelines for efficiency, latency, and reliability.
The engineer will work with engineering teams to integrate AI solutions into cloud-based and on-prem environments, ensuring seamless deployment and scalability.
The position requires automating and maintaining MLOps pipelines, including data preprocessing, model training, evaluation, and deployment.
Implementing monitoring and observability for ML models in production to ensure performance, drift detection, and continuous improvement is also a key responsibility.
The engineer will stay updated on advancements in LLM and voice AI, leveraging state-of-the-art techniques to improve the AI stack.

Requirements:

Candidates must have 5+ years of experience in machine learning engineering, AI infrastructure, or software engineering with a focus on ML deployment.
Strong proficiency in Python and experience with ML frameworks such as TensorFlow, PyTorch, and Hugging Face Transformers is required.
A deep understanding of model deployment and serving technologies (e.g., TensorRT, Triton Inference Server, ONNX, FastAPI) is necessary.
Experience with MLOps tooling (e.g., Kubeflow, MLflow, SageMaker, Vertex AI) and containerization (Docker, Kubernetes) is essential.
Familiarity with cloud platforms (AWS, GCP, Azure) and deploying AI/ML services in production environments is required.
Strong software engineering skills, with experience in designing scalable and maintainable ML systems, are a must.

The position offers the opportunity to build and scale AI models in production that directly impact healthcare efficiency.
It provides a role where engineering meets AI, giving full ownership of ML deployment and optimization.
The work environment is remote-friendly and flexible, prioritizing impact over hours worked.
Competitive salary, equity options, and benefits are included, such as health, dental, and vision coverage.