MACHINE LEARNING(ML) /AI ENGINEER (3)

Overview:
We are seeking an ML Engineer who can build the training and inference infrastructure that makes machine learning models production-ready. This is a hands-on ML engineering role with a strong emphasis on Python, PyTorch, ML pipelines, experiment tracking, benchmarking, and model deployment. You will work across training workflows, inference services, model evaluation, packaging, deployment, and ranking/triage systems that determine when AI can handle a task autonomously versus when it should be routed to a human operator. The ideal candidate is not simply focused on training models—they understand how to build reliable ML systems, establish performance gates, and move models from prototype through production.

Requirements:

1. Must have demonstrated experience building ML pipelines, including training workflows, experiment tracking, model evaluation, benchmarking, or deployment infrastructure.
2. Must have hands-on PyTorch experience, including the ability to demonstrate practical coding and ML implementation skills.
3. Must have demonstrated production ML system experience, including deploying, monitoring, maintaining, or operating models in production environments.
4. Strong understanding of machine learning fundamentals, including model development, training, evaluation, performance measurement, and common ML approaches.
5. Must have a strong software engineering foundation, with approximately 5+ years of software engineering experience and meaningful hands-on ML engineering experience.
6. Proven ability to take ML capabilities from 0?1 prototyping through production hardening, including testing, performance gates, packaging, deployment, and ongoing maintenance.
7. Experience with ranking, retrieval, categorization, or triage models, particularly systems that determine how work is routed between AI systems and human operators.
8. Demonstrated breadth across ML infrastructure, pipelines, evaluation, deployment, and software engineering rather than specialization in only one narrow ML area.
9. Experience with healthcare data including clinical documentation, medical coding, claims, or revenue cycle management (RCM) is strongly preferred.
10. Experience with retrieval systems, search, embeddings, model fine-tuning, distillation, reward modeling, or ML evaluation frameworks is preferred.