Rokt-posted 11 months ago
$215,000 - $270,000/Yr
Full-time • Senior
Hybrid • Seattle, WA
1,001-5,000 employees
Professional, Scientific, and Technical Services

We are looking for a Senior MLOps Engineer to maintain and scale our TensorFlow and Kubeflow-based machine learning pipeline, enhancing its reliability and efficiency. In this role, you will optimize deployment processes to reduce setup time and boost pipeline scalability to support throughput as we scale. Working directly with ML engineers, software engineers, and infrastructure teams, you will form cross-functional units to automate workflows, monitor model performance, and address real-time issues. Leverage your expert software engineering skills and comprehensive understanding of the machine learning lifecycle to drive the seamless integration and ongoing enhancement of our AI solutions, ensuring robust performance in production environments.

  • Enhance ML Pipeline Efficiency: Directly improve the robustness, scalability, and performance of our TensorFlow and Kubeflow pipelines. Focus on optimizing model training and serving processes, minimizing downtime, and automating routine tasks to increase operational efficiency.
  • Drive Platform Scaling & Innovation: Take a leadership role in expanding our ML platform's capacity to manage larger data volumes and more complex models. Research and integrate cutting-edge technologies, develop scalable architectures, and elevate system performance and efficiency through continuous enhancements.
  • Establish MLOps Excellence: Design and implement robust MLOps frameworks that streamline the integration, continuous deployment, and monitoring of ML models. Set up comprehensive CI/CD pipelines, automate testing, and create monitoring tools to proactively track model performance and detect issues.
  • Foster Cross-Functional Collaboration: Partner with data scientists, software engineers, and product teams to transform business requirements into scalable and dependable machine learning solutions. Bridge the gap between model development and deployment, ensuring models are production-ready and align with performance standards.
  • Overcome Production Challenges: Proactively monitor, troubleshoot, and resolve issues affecting model performance, data pipeline integrity, and system efficiency. Identify root causes and implement strategic solutions to ensure the ongoing stability and performance of our ML infrastructure.
  • Expertise in TensorFlow and Kubeflow for machine learning pipeline management.
  • Strong software engineering skills with a focus on MLOps practices.
  • Comprehensive understanding of the machine learning lifecycle and deployment processes.
  • Experience in designing and implementing CI/CD pipelines for ML models.
  • Ability to troubleshoot and resolve production issues effectively.
  • Experience with large-scale data processing and complex model management.
  • Familiarity with cloud platforms and services for ML deployment.
  • Knowledge of monitoring tools and performance tracking for ML systems.
  • Compensation range of $215,000 - $270,000 salary.
  • Employee equity plan grant.
  • World class benefits.
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