Job Description
About the Role
We’re looking for a self-motivated, highly driven Software Engineer II to join our Machine Learning Platform (MLOps) team. As a team, we enable Attentive’s Machine Learning (ML) practice to directly impact Attentive’s AI product suite through the tools to train, inference, and deploy ML models with higher velocity and performance, while maintaining reliability. We build and maintain a foundational ML platform spanning the full ML lifecycle for consumption by ML engineers and data scientists. This is an exciting opportunity to join a rapidly growing ML Platform team at the ground floor with the ability to drive and influence the architectural roadmap enabling the entire ML organization at Attentive.
This team and role is responsible for building and operating the ML data, tooling, serving, and inference layers of the ML platform. We are excited to bring on more engineers to continue expanding this stack.
What You'll Accomplish
Expand, mature, and optimize our ML platform built around cutting edge tooling like Ray, MLFlow, Argo, and Kubernetes to support traditional and deep learning ML modelsBuild and mature capabilities to support CPU / GPU clusters, model performance monitoring, drift detection, automated roll-outs, and improved developer experienceBuild, operate, and maintain a low-latency, high volume ML serving layer covering both online and batch inference use casesOrchestrate Kubernetes and ML training / inference infrastructure exposed as an ML platformExpose and manage environments, interfaces, and workflows to enable ML engineers to develop, build, and test ML models and servicesClose the latency gap on model inference to online, real-time model servingDevelop automation workflows to improve team efficiency and ML stabilityAnalyze and improve efficiency, scalability, and stability of various system resourcesPartner with other teams and business stakeholders to deliver business initiativesHelp onboard new team members, provide mentorship and enable successful ramp up on your team's code basesAbout you
You have been working in the areas of MLOps / Platform Engineering / DevOps / Infrastructure for 3+ years, and have an understanding of gold standard practices and best in class tooling for MLYour 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 needsYou have experience building infrastructure for an ML platform and managing CPU and GPU computeYou have a background in software development and are passionate about bringing that experience to bear on the world of ML infrastructureYou have experience with Infrastructure as Code using Terraform and can’t imagine a world without itYou understand the importance of CI/CD in building high-performing teams and have worked with tools like Jenkins, CircleCI, Argo Workflows, and ArgoCDYou are passionate about observability and worked with tools such as Splunk, Nagios, Sensu, Datadog, New RelicYou 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 administratorYour Expertise
You have been working in the areas of ML Platform / MLOps / Platform Engineering / DevOps / Infrastructure for 3+ years, and have an understanding of gold standard practices and best in class tooling for MLYour 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 needsYou have experience building infrastructure for an ML platform and managing CPU and GPU computeYou have a background in software development and are passionate about bringing that experience to bear on the world of ML infrastructureYou have experience with Infrastructure as Code using Terraform and can’t imagine a world without itYou understand the importance of CI/CD in building high-performing teams and have worked with tools like Jenkins, CircleCI, Argo Workflows, and ArgoCDYou are passionate about observability and worked with tools such as Splunk, Nagios, Sensu, Datadog, New RelicYou 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. What We Use
Our infrastructure runs primarily in Kubernetes hosted in AWS’s EKSInfrastructure tooling includes Istio, Datadog, Terraform, CloudFlare, and HelmOur backend is Java / Spring Boot microservices, built with Gradle, coupled with things like DynamoDB, Kinesis, AirFlow, Postgres, Planetscale, and Redis, hosted via AWSOur frontend is built with React and TypeScript, and uses best practices like GraphQL, Storybook, Radix UI, Vite, esbuild, and PlaywrightOur automation is driven by custom and open source machine learning models, lots of data and built with Python, Metaflow, HuggingFace 🤗, PyTorch, TensorFlow, and Pandas
