Principal Engineer, Machine Learning Ops
New
H
HARMANMusic technology
Position is 100% Remote; Source API remote eligibility restrictions: United States, Available for meetings and team interaction during typical continental US business hours.Full-TimePrincipal
Salary120,750 - 177,100 USD per year
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Job Details
- Experience
- 10+ years across MLOps and DevOps
- Required Skills
- AWSDockerPythonBashGCPKubernetesMicrosoft AzureDevOpsTerraformMLOps
Requirements
- Bring 10+ years of experience across MLOps and DevOps.
- Understand modern MLOps and DevOps tools and best practices, including model training and deployment, experiment management, scalable data pipelines, and real-time or eventually consistent systems.
- Be proficient in Python and bash.
- Have extensive hands-on experience with GCP, AWS, or Azure.
- Have expertise with Docker, Kubernetes, Terraform, and serverless architectures.
- Have experience with data pipelines, infrastructure as code (IaC), monitoring, logging, and alerting systems.
- Know system components including databases, caches, event streaming, queues, data warehouses, and ML-specific data infrastructure.
- Automate infrastructure, deployments, model training pipelines, and system management using industry-standard tools.
- Be familiar with ML engineer development environments, model experimentation workflows, operating data pipelines, and end-to-end ML lifecycle management.
- Be available for meetings and team interaction during typical continental US business hours.
- Be willing to travel up to 5%, domestically and internationally.
- A bachelor's degree in computer science or a related field is a bonus.
- Production machine learning workloads and experience with social products or recommendation systems are bonuses.
Responsibilities
- Develop scalable, maintainable, and operable MLOps infrastructure to support product launch and future growth.
- Make critical early-stage development decisions to support long-term success.
- Provide technical guidance on backend engineering, ML engineering, DevOps, and MLOps considerations.
- Engage with product stakeholders in an iterative environment.
- Collaborate with app developers and engineers to support project delivery.
- Set up development environments for ML engineers and manage model training and experimentation workflows.
- Build and operate data pipelines and automate deployment of recommendation systems.
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