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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Staff Software Engineer, ML Infrastructure - **Company:** Attentive Mobile - **Location:** San Francisco, CA, United States - **Experience:** Expert - **Contract:** Permanent contract - **Skills:** Java (Programming Language), Airflow, Amazon Web Services, Continuous Integration, Data Infrastructure, DevOps, Amazon DynamoDB, Gradle, Python (Programming Language), PostgreSQL, Machine Learning, Nagios, Open Source Technology, Package Management Systems, Redis, Reliability Engineering, Tensorflow, Azure Machine Learning, Software Engineering, Data Streaming, TypeScript, Web Services, Datadog, Circleci, Feature Engineering, Pytorch, ReactJS, Spring-boot, Backend, Pandas, Webpack, Kubernetes, Low Latency, HuggingFace, Playwright, Sensu, Graphql, Machine Learning Operations, Front End Software Development, Terraform, Splunk, New Relic (SaaS), Docker, Jenkins, Microservices - **Published:** November 7, 2026 - **Apply:** https://www.juju.com/job/28_25303_dbc808e5b1a92444f49031de8fbc13fd ## About the Role * You have been working in the areas of MLOps / ML Platform / Data Platform / Site Reliability Engineering / DevOps / Infrastructure for 8+ years, and have an understanding of best practices for DevOps applied to ML * You have successfully led major cross-functional, cross-team ML infrastructure or ML platform projects * Your passion is infrastructure and 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 have deep experience in Kubernetes applied to ML use cases such as CPU & GPU training, hosting and exposing ML tools, and managing ML endpoints as web services * You understand the key differences between online and offline ML inferences and can voice the critical elements to be successful with each * 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. ## Description * Define and lead cross-functional ML infrastructure and ML platform projects * Demonstrate the ability to analyze, troubleshoot, coordinate, and resolve complex ML infrastructure issues * 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, * Design and lead implementation of an online inference pipeline with champion/challenger model testing * Unite existing pipelines across data, ML, and platform teams to handle low-latency, high volume real-time streaming use cases in production inference workflows * Define golden path build and release pipelines for better reliability and Python package management * Identify opportunities to improve scalability, resiliency, and cost efficacy of GPU training and inference workflows * Design and lead implementation of a low-touch, automated model CI/CD pipeline, * Our backend is Java / Spring Boot microservices, built with Gradle, coupled with things like DynamoDB, Pulsar, AirFlow, Postgres, Planetscale, and Redis, hosted via AWS * Our frontend is built with React and TypeScript, and uses best practices like GraphQL, Storybook, Radix UI, Vite, esbuild, and Playwright * Our automation is driven by custom and open source machine learning models, lots of data and built with Python, Metaflow, HuggingFace , PyTorch, TensorFlow, and Pandas * You'll get competitive perks and benefits, from health & wellness to equity, to help you bring your best self to work.