Senior Cloud Platform Engineer (SMTS)

Salesforce•San Francisco, CA

7d•$148,500 - $246,000•Hybrid

About The Position

As a Senior Member of Technical Staff (SMTS) within our Monitoring Cloud team, you will be a key owner and operator of the systems that keep Salesforce reliable. You won't just be "using" tools; you will be productizing infrastructure to ensure our monitoring capabilities evolve at the scale of our multi-cloud footprint. Your mission is to bridge the gap between high-level feature design and deep-system stability. From automating the "paved path" across AWS and GCP to securing air-gap environments for our most sensitive customers, you will ensure our monitoring stack is invisible, resilient, and intelligent. This is an AI-first engineering role. You will use AI-assisted development tools (e.g., Claude Code) as the default for every inner-loop activity, code authoring, Terraform and Kubernetes scaffolding, test generation, refactoring, log/trace analysis, runbook drafting, and documentation. We expect AI to compound your throughput on routine implementation so you can focus your human judgment on architecture, security, on-call response, and customer outcomes.

Requirements

5+ years Proven track record in Distributed systems, API platforms, Infrastructure Engineering, Observability or DevOps at scale.
Proficiency with Kubernetes (K8s) and Terraform.
Hands-on experience managing infrastructure in AWS and/or GCP.
Proficiency in programming languages(eg: java, python etc)
Experience managing or extending monitoring tools (e.g., Grafana), messaging systems (kafka etc), elastic search, caching frameworks
Security First: Understanding of authN/authZ security protocols, particularly in managing isolated or restricted network environments.
AI-assisted development fluency: demonstrated use of AI coding assistants (e.g., Claude Code) as part of a daily engineering workflow, able to prompt effectively, critically evaluate generated code, and integrate AI into IaC, testing, and automation pipelines.

Responsibilities

Design and implement automation frameworks using Terraform and Kubernetes to manage monitoring infrastructure.
Standardize "paved path" deployments across AWS and GCP, eliminating manual configuration errors and ensuring global consistency.
Use AI-assisted tooling as the default for authoring, refactoring, and reviewing IaC modules, Helm charts, and automation scripts while directing intent, validating output, and owning the final result.
Own the lifecycle of the Monitoring Cloud stack, including version upgrades and performance tuning.
Productize core components (e.g., Grafana, custom Terraform providers) to make them consumable as reliable services by internal engineering teams.
Leverage AI for upgrade planning, release-note analysis, migration scaffolding, and boilerplate-heavy productization work (API wiring, schema plumbing, SDK generation), while retaining accountability for design and rollout.
Deploy and manage the full monitoring stack within highly isolated, air-gapped environments.
Ensure that our most secure customer segments receive the same level of observability and reliability as our public cloud offerings.
Apply AI assistance during development of the artifacts that ship into these environments; operate them in-network with the disciplined, human-driven workflows these environments require.
Participate in the team’s on-call rotation, providing the deep technical expertise required to maintain strict SLAs and availability targets.
Conduct root-cause analysis (RCA) for complex system failures and implement long-term preventative fixes.
Address support requests with a “customer first” mindset
Use AI as a co-pilot during incident response and RCA: summarizing logs, correlating traces, proposing hypotheses, and drafting status updates and postmortem while the engineer remains the accountable responder and decision-maker.
Design and deliver platform features that adhere to enterprise standards while pioneering AI-driven development practices to accelerate delivery and enhance system intelligence.
Contribute to and evolve the team's AI-assisted development playbook: prompts, agents, skills, evaluation harnesses, and guardrails that let the team ship faster without sacrificing quality or security.