Senior Machine Learning Engineer, Rad AI

• 6+ years of experience in ML Systems Engineering
• 6+ years of industry experience writing in Python (preferable) or other common languages in the ML domain
• Strong experience with infrastructure and DevOps tools such as Kubernetes, Docker, and Ansible
• Experience in distributed systems, storage systems, and databases
• Strong knowledge of cloud computing platforms such as AWS (preferable), GCP, and Azure
• Experience with infrastructure-as-code tools such as Terraform (preferable), Pulumi, Cloud Formation, etc
• Experience with monitoring, tracing, and logging tools such Cloudwatch, NewRelic, Prometheus, etc
• Excellent communication skills, with a strong sense of ownership and a systematic approach to problem-solving
• Proven ability to manage and lead active incidents, address what caused them, and establish systems to avoid them in the future via blameless postmortems

Desirable

• Design, implement, and maintain the infrastructure that supports our machine learning applications, services, and workflows
• Build, maintain, and improve our ML platform that supports continuous integration, continuous delivery, and continuous training for our machine learning models
• Leverage low-level programming languages, cloud native services, and serverless architectures to build scalable and resilient systems
• Plan, design and develop components in the data pipeline to enable various machine learning models in production
• Lead the design and implementation of infrastructure projects, including the development of technical designs, plans, and specifications, along with their evolutions and updates
• Design, deploy, and maintain the full ML platform stack including capabilities such as monitoring and data observability, the full model lifecycle, etc
• Investigate the existing pipeline, identify bottlenecks and optimize the throughput and latency of ML components
• Balance metrics and alerting with cost efficiency and detail
• Develop and implement automation tools for model training and deployment