Senior ML Ops Engineer

Anywhere in the United States Apply Our mission at Greenhouse is to make every company great at hiring – so we go to great lengths to hire great people because we believe that they’re the foundation of our success. At Greenhouse, you’ll join a team that collaborates purposefully, fosters inclusivity, and communicates with transparency and accountability so we can help companies measurably improve the way they hire. Join us to do the best work of your career, solving meaningful problems with remarkable teams. Greenhouse is looking for a Senior ML Ops Engineer to join our team! As a senior member of our Applied Machine Learning team, you'll own the critical infrastructure and processes that bring our machine learning models to life. You will be responsible for the full model lifecycle, from working with prototypes of ML and LLM-based solutions to transforming them into reliable, production-ready systems. You'll create, manage, and improve our continuous integration and delivery pipelines that enable rapid iteration and deployment. Learn more about our engineering culturehere!

Who will love this job

• An ML Evangelist– You stay in the know of ML industry trends and enable your team’s ability to work with ML tools
• An Architect – You excel at creating, managing, and improving robust continuous integration and delivery pipelines to support rapid deployment of ML models
• A Reliability Advocate – You are passionate about system uptime, implementing observability practices like monitoring, logging, and alerting, and ensuring high data quality and performance in production
• A Collaborator – You excel at working closely with others across an org to align on requirements and deliver solutions

What you'll do

• Operationalize ML and LLM-based workloads, gather stakeholder feedback, and drive them into production-ready systems
• Create, manage, and improve continuous integration and delivery pipelines to support rapid iteration and deployment of ML models and services
• Implement observability practices for ML systems, including monitoring, logging, and alerting
• Maintain and improve the infrastructure for ML/LLM evaluation sets, including versioning, automated validation pipelines, and continuous quality checks across the model lifecycle
• Ensure high data quality and monitor performance of ML workloads in production
• Work closely with ML engineers, data scientists, and cross-functional teams to build impactful solutions
• Participate in an on-call rotation to ensure system uptime and reliability
• Additional projects and responsibilities as business needs require

You should have

• Proven experience in an MLOps, DevOps, or a related software engineering field
• Experience implementing safe, ethical, and compliant ML systems (familiarity with ISO 42001/NIST AI RMF and the associated common controls)
• Strong cloud infrastructure experience with AWS
• A deep understanding of Kubernetes
• Expertise with IaC (Infrastructure as Code) & GitOps Tools like Terraform and Argo CD
• Experience developing and augmenting CI/CD Pipelines
• A strong understanding of the state of the art in machine learning, especially LLMs
• Familiarity with ML frameworks such as PyTorch, MLFlow, vLLM, Transformers, and Torch
• Practical experience managing data quality and performance in production ML environments
• Experience designing data architectures optimized for AI/ML, such as with Opensearch (vector databases)
• Familiarity with tools and platforms like Bedrock/Sagemaker,...