Staff ML Engineer

HCA Healthcare; United States; Remote; USD 150,000 - 190,000; Full time

Job summary

HCA Healthcare is seeking a Staff Machine Learning Engineer to lead the implementation and adoption of AI Platform capabilities within an embedded product team. You will act as a technical leader and mentor for other ML engineers, while actively contributing to AI/MLOps development through hands-on coding and by standardizing implementation patterns.

You will collaborate with platform and product managers to define standards, drive platform adoption, and implement robust ML pipelines and

Qualifications

• Bachelor's degree required in a related field.
• Master's degree preferred.
• 7+ years in software engineering with a focus on ML/AI systems.
• Embedded engineer experience in a cross-functional product team preferred.

Responsibilities

• Partner with platform and product managers to identify foundational platform capabilities.
• Define technical standards and patterns across platforms and product teams.
• Collaborate on technical and architectural direction for platform components.
• Mentor embedded MLEs in engineering best practices and platform tooling.
• Implement and scale CI/CD pipelines for ML models.
• Develop ML systems using platform capabilities and drive adoption.

Skills

ML engineering

Python for ML

Platform adoption

CI/CD for ML

ML workflows

ML lifecycle & MLOps

Python practices

Collaboration with product teams

Distributed training

Orchestrators (Kubeflow/Argo/MLFlow)

Model registries

IaC (Terraform)

GCP Vertex AI

Feature stores & serving

Platform tooling

Education

Bachelor's degree

Master's degree

Tools

Kubeflow

Argo

MLFlow

Terraform

Job description

The Staff Machine Learning Engineer leads technical implementation and adoption of AI Platform capabilities within an embedded product team while serving as a technical leader and mentor for other Machine Learning Engineers. Actively contributes to AI Platform and operations development through hands‑on coding while establishing best practices, standardizing implementation patterns, and driving platform adoption. Advocates for platform solutions, ensuring consistent application of engineering standards, and accelerating AI delivery through effective platform usage and technical mentorship.

Major Responsibilities:

• Partner with platform and product managers to identify and prioritize foundational platform capabilities
• Informs the definition and implementation of technical standards and patterns across the platforms and product teams.
• Collaborates on technical and architectural direction for critical platform components
• Participates in technical discussions and decision‑making processes for key platform features
• Mentors embedded MLEs in engineering best practices and platform tooling and adoption.
• Helps evaluate and make recommendations on technical approaches and new technologies
• Actively contribute to AI/MLOps development within assigned product team
• Drive adoption of platform capabilities through example and technical guidance
• Implement and validate platform patterns within pod
• Help identify and solve common challenges across pods
• Balance pod‑specific needs with platform standardization
• Champion platform adoption within pod and across teams
• Provide technical guidance on platform usage and implementation
• Identify opportunities for leveraging platform capabilities
• Contribute to platform feature development and improvement
• Help validate and refine platform patterns through direct implementation
• Share knowledge and best practices across the MLE team
• Implement robust CI/CD pipelines for ML models
• Develop and maintain ML systems using platform capabilities
• Ensure proper testing and validation of ML systems
• Document technical decisions and implementation patterns
• Partner with platform team to improve developer experience
• Conduct thorough code reviews with focus on platform patterns
• Contribute to technical design discussions and architecture reviews

Education & Experience:

• Bachelor's degree - Required
• Master's degree - Preferred
• 7+ years of experience in software engineering with a focus on ML and AI System Engineering - Required
• Experience working in as an embedded engineer in a cross function product team - Preferred

Knowledge, Skills, Abilities, Behaviors:

• Strong technical background in ML engineering with demonstrated coding expertise - Required
• Track record of driving adoption of technical platforms and developer tools - Required
• Deep Python development expertise with focus on ML systems and AI/MLOps - Required
• Proven ability to establish and maintain technical standards - Required
• Strong understanding of ML workflows and operational requirements - Required
• Hands‑on experience implementing and scaling model CI/CD pipelines - Required
• Experience with modern Python development practices including type checking, testing frameworks, and package management - Required
• Experience with modern Python development practices including type checking and testing - Required
• History of successful collaboration with product teams - Required
• History of successful collaboration with product teams - Required
• Understanding of ML Development Lifecycle management and MLOps best practices - Required
• Understanding of ML Monitoring and observability - Required
• Experience with LLMs and Infrastructure - Preferred
• Experience integrating with feature stores, feature caches and model serving platforms - Preferred
• Deep understanding of ML/AI platform tooling and patterns - Preferred
• Experience with Distributed model training - Preferred
• Hands on experience with Kubeflow, Argo, MLFlow or other ML/AI Training orchestrators - Preferred
• Hands on experience and knowledge of ML/AI metadata tools and model registries - Preferred
• Deep hands‑on experience with Terraform or other IaC tools - Preferred
• Hands on experience building ML/AI solutions on GCP and Vertex AI - Preferred

Work Location/Schedule:

• Remote (U.S. Only) - M-F, 8am - 5pm - Central Time

Travel Required:

• This job requires travel to Nashville, TN to attend final interview, 3-day New Hire Orientation, quarterly team meetings, and other travel on an as‑needed basis
• Not offered, now or in the future

Virtual Interviews:

• All HCA Healthcare virtual interviews are conducted without the use of virtual backgrounds, background blurring, or any other visual distortion tools.

AI Usage Policy:

• The use of AI tools or prompts during any part of the interview is strictly prohibited. If it appears that you are reading from a script or using AI‑generated responses, you will be eliminated from further consideration. We value authenticity and want to hear your own thoughts and experiences.