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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Senior Backend Software Engineer, Developer Productivity Engineering - **Company:** Tapestry, Inc. - **Location:** Mountain View, CA, United States - **Experience:** Expert - **Salary:** $174,000.0 - $255,000.0 - **Contract:** Permanent contract - **Skills:** Testing (Software), Artificial Intelligence, Apache HTTP Server, Big Data, BigQuery, Cloud Computing, Cloud Engineering, Code Review, Continuous Integration, Data Infrastructure, DevOps, Microprocessors, Disaster Recovery, Github, Identity and Access Management, Machine Learning, Reliability Engineering, Distributed Simulation, Software Engineering, Datadog, Pulumi, Graphics Processing Unit (GPU), Google Cloud, Cloud Platform System, Autoscaling, Delivery Pipeline, Large Language Models, Snowflake, Apache Spark, Mttr, Infrastructure as Code (IaC), Backend, Agentic-AI, Kubernetes, Deployment Automation, Apache Kafka, Gsuite, Terraform, Dynatrace, Automation Anywhere, Vulnerability Analysis - **Published:** November 5, 2026 - **Apply:** https://www.careerbuilder.com/job-details/senior-backend-software-engineer-developer-productivity-engineering-mountain-view-ca--22ac64f3-f523-4657-890e-409fcfc5348b ## About the Role Software Engineering, Software Development, Artificial Intelligence (AI), Apache Tapestry, Google Apps, Software Engineering, Ecosystems, Process Improvement, Electricity, Cross-Functional, * Technical Breadth & Depth: 8+ years of production experience in Infrastructure, Site Reliability Engineering, DevOps, or Developer Productivity/Platform Engineering. * Modern Cloud & Orchestration: Advanced production-grade expertise with Kubernetes (GKE), container networking, and multi-tenant cloud architectures (Google Cloud Platform preferred). * Infrastructure as Code (IaC): Deep architectural experience with modern declarative tools (Terraform, Pulumi, or similar) managing complex multi-environment cloud footprints. * CI/CD & Developer Experience Primitives: Proven track record building large-scale, automated build/test/release pipelines (e.g., Tekton, GitHub Actions, Argo Workflows, Bazel) designed around self-service internal developer platforms. * Platform-as-a-Product Mindset: Demonstrated ability to interview internal engineering stakeholders, quantify developer friction points, and deliver platforms that measurably increase overall deployment frequency and reduce MTTR., * AI & Agentic Workloads: Practical experience designing infrastructure, sandboxes, and execution runtimes specifically geared toward AI agents, LLM evaluations, or high-performance GPU orchestration. * Data Platform Adjacency: Hands-on architectural exposure to supporting large-scale data platforms (e.g., BigQuery, Spark, Kafka, Ray) or complex distributed simulation environments. * Alphabet Ecosystem: Working knowledge of Google-internal infrastructure primitives (Borg, Monarch, Spanner, Piper/Blaze) or experience operationalizing an X moonshot into an independent production environment. * Tier-1 Tech Platform Experience: Background engineering high-throughput platform tooling or developer infrastructure at companies operating at high engineering scale (e.g., Netflix, Snowflake, LinkedIn, Datadog). ## Description We are seeking a Senior Backend Software Engineer to join our Infrastructure and Developer Productivity team. Our systems must process massive amounts of power-grid data, run heavy scientific simulations, and support emerging AI workflows. You will treat our internal infrastructure as a product. Building reliable, self-service tools for our developers, automating our delivery pipelines, and creating secure environments where engineers and automated agents can build and test software quickly and safely., * Core Cloud & Compute Platform: Architect and operate scalable, multi-tenant Kubernetes clusters on Google Cloud Platform (GCP). Ensure efficient compute scheduling and autoscaling across mixed hardware workloads (CPUs, GPUs, and TPUs) supporting simulation and machine learning models. Design resilient networking, IAM boundaries, and secure multi-project cloud environments. * Developer Velocity & CI/CD: Design fast, hermetic, and automated build, test, and release pipelines that reduce cycle times for product engineers. Provide reliable, on-demand testing environments so teams can validate changes safely before production. Build automated deployment and rollback mechanisms with clear canary verification. * Infrastructure as Code & Security: Drive declarative, reproducible cloud infrastructure using modern Infrastructure-as-Code (such as Terraform). Implement automated policy checks, secret management, and container vulnerability scanning into standard deployment workflows. * AI Tooling & Execution Sandboxes: Design secure, isolated sandbox environments that allow automated AI tools and agents to safely run tests and inspect code. Identify high-leverage opportunities to automate repetitive developer workflows using modern AI tools. * Technical Co-Leadership & Mentorship: Partner with the team to split architectural ownership, guide system designs, and run engineering design reviews. Mentor mid-level and senior engineers (L4/L5), raising the technical bar for code reviews, testing, and system design. * Reliability & Observability: Define team-wide standards for metrics, logs, and distributed tracing to ensure high visibility into production health. Partner with data and ML teams to establish SLAs/SLOs, lead disaster recovery exercises, and run blameless post-mortems.