Senior II Software Engineer, Machine Learning Platform

What you'll do

• Build and maintain core machine learning platform services including model serving, training pipelines, and experiment tracking.
• Evolve the platform from a collection of tools into a coherent, self-service product for internal users.
• Improve infrastructure scalability, reliability, and operability while managing costs and technical complexity.
• Enhance observability and monitoring across the model lifecycle to help data scientists track performance.
• Participate in on-call rotations to ensure platform stability and reduce operational toil.
• Contribute to the technical roadmap through architectural decisions, discovery work, and exploratory spikes.
• Mentor team members and improve engineering standards through code reviews and documentation.

What we're looking for

• Strong engineering background in mainstream languages like Java or Python (preferably Python) for building and maintaining production systems.
• Experience with platform or infrastructure engineering, including internal developer platforms or self-service tooling.
• Experience with Kubernetes for deploying, managing, and troubleshooting containerized workloads.
• Experience with cloud infrastructure, ideally AWS, covering compute, storage, and networking.
• Strong understanding of distributed systems principles to make pragmatic architectural decisions.
• Experience with observability and monitoring, including building dashboards, alerts, and health-tracking tools.
• Solid understanding of software engineering best practices such as testing, code review, CI/CD, and clean code.
• Ability to use AI-assisted development tools responsibly while maintaining ownership of code quality.
• Familiarity with ML platform tooling like MLflow or Airflow (preferred).
• Familiarity with ML systems and workflows including training, serving, and feature engineering (preferred).
• Experience with Infrastructure as Code such as Terraform or CDK (preferred).
• Exposure to streaming or batch data processing frameworks like Spark, Flink, or Kafka (preferred).