[Hiring] Senior ML Engineer II @Waystar

Role Description

We are seeking a highly skilled and innovative Senior ML Engineer with a passion for building robust, efficient, and domain-specific AI systems using Language Models (LMs) and agentic architectures. As a core member of the team, you will be instrumental in developing the entire ML pipeline, from sophisticated data extraction techniques to fine-tuning specialized LMs and orchestrating their interactions within a multi-agent framework. This is a unique opportunity to apply state-of-the-art Generative AI and NLP techniques to a real-world, high-impact problem, leveraging the latest research in agentic AI and LMs to deliver economical and powerful solutions.

Qualifications

• Bachelor's or Master's degree in Computer Science, Machine Learning, Artificial Intelligence, Statistics, or a related quantitative field. Ph.D. preferred.
• 5+ years of professional experience in machine learning engineering, with a strong track record of deploying and maintaining ML models in production environments.
• Expertise in programming languages such as Python (with extensive experience in ML libraries like TensorFlow, PyTorch, Scikit-learn).
• Deep understanding of machine learning fundamentals, including supervised, unsupervised, and reinforcement learning techniques, as well as deep learning architectures.
• Strong experience with cloud platforms (AWS, Azure, GCP) and their ML services.
• Proficiency in building and managing data pipelines using tools like Spark, Kafka, SQL, and NoSQL databases.
• Demonstrated experience with MLOps principles and tools (e.g., MLflow, Kubeflow, Sagemaker, Airflow).
• Excellent problem-solving skills and the ability to work independently on complex issues.
• Strong communication and interpersonal skills, with the ability to collaborate effectively in a cross-functional team.
• Experience in the healthcare technology domain is a significant plus.
• Proven ability to lead technical initiatives and influence architectural decisions.

Requirements

• Design, implement, and optimize robust pipelines for ingesting, parsing, and extracting structured information from complex documents (leveraging OCR, document layout analysis, Named Entity Recognition (NER), and Relationship Extraction (RE)).
• Develop rich, nested JSON schemas for representing structured data and ensure scalable storage.
• Generate and manage high-quality vector embeddings for efficient retrieval-augmented generation (RAG) within a Vector Database.
• Research, select, and experiment with appropriate open-source Language Models (Large & Small) (e.g., Phi-3, Mistral, Llama, Nemotron-H families) for specialized tasks.
• Design and execute efficient fine-tuning strategies (e.g., LoRA, QLoRA, full fine-tuning) on curated, domain-specific datasets to achieve precise performance for tasks like coverage determination, code lookups, and policy rule application.
• Explore and implement knowledge distillation techniques to transfer capabilities from larger models to smaller, more efficient LMs.
• Build and maintain the core agentic framework, including the orchestrator that intelligently routes queries and coordinates interactions between various specialized LM tools.
• Develop and integrate "tools" (specialized LMs and external APIs) that perform atomic medical necessity tasks, ensuring strict behavioral alignment and structured outputs.
• Deploy, manage, and monitor LMs and agentic components on Google Cloud Platform (GCP) using services like Vertex AI, GKE, Cloud Functions, and Cloud Run.
• Implement robust MLOps practices for continuous integration, continuous delivery (CI/CD), model versioning, and performance monitoring (latency, throughput, accuracy).
• Establish effective feedback loops from end-user interactions and system logs to identify areas for model improvement.
• Curate and expand training datasets, ensuring data privacy (PHI/PII masking) and legal compliance.
• Stay abreast of the latest research in LMs, agentic AI, NLP, and document understanding, applying relevant advancements to our system.
• Work closely with subject matter experts, product managers, and other engineers to translate complex requirements into technical solutions and evaluate system performance.

Benefits

• Competitive total rewards (base salary + bonus, if applicable).
• Customizable benefits package (3 medical plans with Health Saving Account company match).
• Generous paid time off for non-exempt team members, starting with 3 weeks + 13 paid holidays, including 2 personal floating holidays.
• Flexible time off for exempt team members + 13 paid holidays.
• Paid parental leave (including maternity + paternity leave).
• Education assistance opportunities and free LinkedIn Learning access.
• Free mental health and family planning programs, including adoption assistance and fertility support.
• 401(K) program with company match.
• Pet insurance.
• Employee resource groups.