Senior ML Engineer II

Waystar, Inc; Atlanta (GA); On-site; USD 120,000 - 160,000; Full time

Benefits offered by this job

Competitive salary and bonus

Customizable benefits package

Generous paid time off and holidays

Paid parental leave

401(K) program with company match

Education assistance

Mental health and family planning programs

Pet insurance

Job summary

Waystar, Inc is looking for a highly skilled Senior ML Engineer to develop state-of-the-art AI systems. You'll design and optimize data pipelines, research advanced ML models, and lead deployment in a Google Cloud Platform environment.

To succeed, you should have a strong background in machine learning, programming expertise in Python, and a knack for collaboration. The role offers a comprehensive benefits package and flexible time off.

Qualifications

• 5+ years of professional experience in machine learning engineering.
• Strong programming skills in Python and ML libraries.
• Experience deploying and maintaining ML models in production.

Responsibilities

• Design and implement robust data pipelines.
• Research and develop fine-tuning strategies for language models.
• Deploy and manage ML components on Google Cloud Platform.

Skills

Machine Learning

Python

NLP

TensorFlow

MLOps

Cloud Services

Data Pipelines

Healthcare Technology

Education

Bachelor's or Master's degree in Computer Science or related field

Ph.D. in relevant field preferred

Tools

Google Cloud Platform

Spark

Kafka

SQL

NoSQL

Job description

Overview

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.

Responsibilities

• Data Pipeline & Knowledge Base Construction:

  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.

• Language Model (LM) Development & Fine‑tuning:

  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.

• Agentic System Design & Implementation:

  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.

• MLOps & Deployment:

  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).

• Continuous Improvement & Research:

  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.

• Collaboration:

  Work closely with subject matter experts, product managers, and other engineers to translate complex requirements into technical solutions and evaluate system performance.

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.

Benefits

• Competitive total rewards (base salary + bonus, if applicable)
• Customizable benefits package (3 medical plans with Health Savings Account company match)
• Generous paid time off for non‑exempt members, starting with 3 weeks + 13 paid holidays, including 2 personal floating holidays; flexible time off for exempt 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

EEO Statement

Waystar is proud to be an equal opportunity workplace. We celebrate, value, and support diversity and inclusion. Qualified applicants will receive consideration for employment without regard to race, color, religion, age, sex, national origin, disability status, genetics, marital status, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by federal, state, or local laws. This applies to all terms and conditions of employment, including recruiting, hiring, placement, promotion, termination, layoff, recall, transfer, leaves of absence, compensation, and training.