Software Engineering MTS

Salesforce•Palo Alto, CA

2d•$117,200 - $194,200•Hybrid

About The Position

The Software Engineer (MTS) role is part of our Platform Engineering team within the Cloud Infrastructure organization. Platform Engineering is made up of platform engineers, SREs, and DevOps specialists who design, build, and operate the internal developer platform powering hundreds of Kubernetes clusters across AWS, Azure, GCP, and OCI. Whether we are automating cluster lifecycle management, hardening our GitOps delivery pipelines, or embedding AI into our daily engineering workflows, we strive to give every product team a fast, secure, and reliable path to production. We are looking for a Member of Technical Staff to grow with our multi-cloud platform. You’ll build and operate core platform services in Go and Python, contribute to our GitOps and continuous deployment pipelines, and learn to operate Kubernetes at scale — all while developing an agentic mindset and becoming an AI amplifier who multiplies your own output with AI-assisted engineering practices from day one.

Requirements

1+ years of professional experience in cloud infrastructure engineering and continuous deployment.
Hands-on Kubernetes experience — deploying, troubleshooting, and automating workloads or clusters (networking, scaling, upgrades).
Good programming skills in Golang and/or Python, with experience building services, CLIs, or automation tooling.
Multi-cloud exposure, primarily AWS, with a working understanding of core compute, networking, IAM, and managed Kubernetes services.
Familiarity with GitOps using Flux or Argo CD, and infrastructure-as-code with Pulumi (or comparable tooling such as Terraform).
Demonstrated AI literacy and an emerging agentic mindset — you actively use AI tools in your daily engineering work (code generation, debugging, documentation, automation) and can speak to how AI has amplified your output.
Strong communication and collaboration skills, and an eagerness to learn and grow within a fast-moving platform team.

Nice To Haves

Hands-on experience with Claude Code or other agentic coding tools — building agentic workflows, custom commands, or MCP integrations is a strong plus; any track record of being an AI amplifier stands out even more.
Experience with GCP, OCI, or Azure beyond AWS, especially managing Kubernetes (GKE, OKE, AKS) across providers.
Exposure to compliance-driven environments (FedRAMP, SOC 2) and sound security and change-management practices.
Familiarity with policy-as-code (Kyverno, OPA/Gatekeeper), supply-chain security (cosign, SBOM), or artifact/registry governance.
Exposure to internal developer platforms (IDPs), platform APIs, or developer experience tooling.
Contributions to open-source cloud-native projects (CNCF ecosystem).

Responsibilities

Build and maintain platform services and automation in Go and Python that help manage Kubernetes clusters across AWS, Azure, GCP, and OCI.
Contribute to continuous deployment pipelines using GitOps tooling (Flux, Argo CD) and infrastructure-as-code frameworks (Pulumi, Terraform) to enable safe, repeatable releases.
Take part in fleet initiatives such as cluster lifecycle automation, upgrades, policy enforcement, and observability improvements, with guidance from senior engineers.
Partner with SRE, security, and product engineering teams to understand their needs and help deliver self-service platform capabilities that reduce toil.
Be an AI amplifier: use AI tooling in your everyday engineering workflows — agentic coding assistants (e.g., Claude Code) for development, code review, automation, and operational runbooks — to force-multiply your output as you ramp.
Develop an agentic mindset: learn to spot repetitive operational work and propose agent-driven or AI-augmented automations that reduce manual effort.
Participate in design reviews and code reviews, write clear technical documentation, and grow your platform and cloud-native expertise.
Join on-call rotations as you ramp and help improve the operational posture of the platform through automation and post-incident learning.