Senior Software Engineer, Machine Learning Operations

Why Attentive needs you

• Demonstrate the ability to analyze, troubleshoot, coordinate, and resolve complex infrastructure issues
• Build, operate, and maintain a low-latency, high volume ML serving layer covering both online and batch inference use cases
• Orchestrate Kubernetes and ML training / inference infrastructure exposed as an ML platform
• Expose and manage environments, interfaces, and workflows to enable ML engineers to develop, build, and test ML models and services
• Manage and expand our feature store implementation that allows ML teams to self-service data labeling, feature engineering, and batch inferencing
• Close the latency gap on model inference to online, real-time model serving
• Develop automation workflows to improve team efficiency and ML stability
• Analyze and improve efficiency, scalability, and stability of various system resources
• Partner with other teams and business stakeholders to deliver business initiatives
• Help onboard new team members, provide mentorship and enable successful ramp up on your team's code bases

About you

• You have been working in the areas of MLOps / Platform Engineering / DevOps / Infrastructure for 5+ years, and have an understanding of gold standard practices and best in class tooling for ML
• Your passion is exposing platform capabilities through interfaces that enable high performance ML practices, rather than designing ML experiments (this team does not directly develop ML models)
• You understand the key differences between online and offline ML inferences and can voice the critical elements to be successful with each to meet business needs
• You have experience building infrastructure for an ML platform and managing CPU and GPU compute
• You have a background in software development and are passionate about bringing that experience to bear on the world of ML infrastructure
• You have experience with Infrastructure as Code using Terraform and can’t imagine a world without it
• You understand the importance of CI/CD in building high-performing teams and have worked with tools like Jenkins, CircleCI, Argo Workflows, and ArgoCD
• You are passionate about observability and worked with tools such as Splunk, Nagios, Sensu, Datadog, New Relic
• You are very familiar with containers and container orchestration and have direct experience with vanilla Docker as well as Kubernetes as both a user and as an administrator

Our environment

• We have access to Python, Snowflake, SQL, DBT
• Our data visualization tool is Looker
• Our product backend is Java and Python microservices coupled with Spark, Kinesis, Airflow, Snowflake, and Postgres, all hosted on AWS
• Our team supports stakeholders from Client Strategy, Product Management, Sales, Marketing, Finance, Engineering, Designers and the Leadership Team
• We believe our company will win in the long run through product innovation and data-driven decision-making