Remote Senior MLOps Engineer

Summary

Join our team as a skilled MLOps Engineer to operationalize and automate machine learning workflows, ensuring scalability, reliability, and efficiency.

Requirements

• Bachelor's degree in Computer Science, Engineering, Mathematics, Statistics, or a related field
• Proven experience (5+ years) as a MLOps Engineer, Software engineer, DevOps Engineer or related role
• Excellent communication and collaboration skills, with the ability to work effectively in cross-functional teams
• Strong understanding of machine learning concepts, algorithms, and frameworks such as MLFlow, TensorFlow, PyTorch, or Scikit-learn
• Knowledge of big data processing technologies such as Apache Spark for handling large-scale data and distributed computing
• Experience with cloud platforms such as AWS, Azure, or Google Cloud Platform (GCP) and familiarity with services like AWS SageMaker, Azure Machine Learning, or Google AI Platform
• Understanding of containerization technologies like Docker and container orchestration tools like Kubernetes for managing machine learning workflows in production environments
• Proficiency in version control systems (e.g., Git) and CI/CD tools for automating the deployment and management of machine learning models

Responsibilities

• Design, implement, and maintain scalable MLOps infrastructure and pipelines using Apache Spark, Python, and other relevant technologies
• Collaborate with data scientists and software engineers to deploy machine learning models into production environments
• Develop and automate CI/CD pipelines for model training, testing, validation, and deployment
• Implement monitoring, logging, and alerting solutions to track model performance, data drift, and system health
• Optimize and tune machine learning workflows for performance, scalability, and cost efficiency
• Ensure security and compliance requirements are met throughout the MLOps lifecycle
• Work closely with DevOps teams to integrate machine learning systems with existing infrastructure and deployment processes
• Provide technical guidance and support to cross-functional teams on best practices for MLOps and model deployment
• Stay updated on emerging technologies, tools, and best practices in MLOps and machine learning engineering domains
• Perform troubleshooting and resolution of issues related to machine learning pipelines, infrastructure, and deployments

Benefits

• Competitive compensation
• Exceptional benefits, including medical, dental and vision, FSA
• 401k with company matching up to 4%
• Generous vacation policy
• Remote-first & hybrid work philosophies
• A hybrid work schedule (2 days in office & 3 days work from home)
• Modern open office in beautiful San Mateo, CA; Los Angeles, CA; San Diego, CA; Austin, TX and Dallas, TX
• Subsidized gym membership
• Catered, free lunches
• Parties, picnics, and wine-downs
• Free parking