Staff Platform Engineer

Amplitude•San Francisco, CA

2d•$198,000 - $331,000

About The Position

Amplitude's Cloud Platform team builds the systems that every Amplitude engineer relies on every day to ship code — and we're rebuilding them for the AI era. As a Staff Platform Engineer, you'll set technical direction for the platform across teams, lead our highest-complexity and highest-leverage initiatives, and shape a platform where AI agents are first-class users alongside humans: kicking off deploys, opening pull requests against infrastructure, and triaging incidents, so a single engineer can get the throughput of a team. You'll operate across team boundaries — partnering with product engineering, fellow Staff+ engineers, and engineering leadership to make Kubernetes and cloud infrastructure effortless across the entire engineering org. You'll build the self-service automation, shared standards, and scalable AWS and GCP infrastructure that let dozens of product teams ship faster, safer, and with less cognitive load — and you'll multiply the engineers around you while you do it.

Requirements

8+ years of experience in software engineering, DevOps, or Site Reliability Engineering, with deep hands-on time in cloud infrastructure.
Bachelors Degree in Computer Engineering or related field
A track record of leading high-complexity, cross-team infrastructure initiatives that delivered measurable improvements in reliability, developer productivity, performance, or cost.
Deep production experience operating Kubernetes (EKS, GKE, AKS, or on-prem) and containerized applications at meaningful scale.
Strong programming ability in at least one language (Golang or Python preferred) and fluency with IaC tooling (Terraform).
Solid command of AWS core services (EC2, EKS, IAM, VPC, ALB, S3) — GCP and/or Azure experience a plus — and networking/security fundamentals.
Deep familiarity with GitOps workflows and the CNCF ecosystem (Argo, Helm, Backstage, Envoy, and friends), and a point of view on where they're heading.
A history of setting technical standards and patterns that other teams adopted — influencing outcomes well beyond your own keyboard, often without direct authority.
Curiosity and conviction about AI as a force multiplier in infrastructure work — whether that's using AI-assisted development tools to ship faster or building platforms and tooling that help your teammates get more leverage from AI in their day-to-day work.
Strong communicator — you can break down complex topics for varied audiences, drive cross-team decisions to a timely close, and default to collaborative problem solving.
Comfort navigating ambiguity by validating assumptions early and using data to de-risk technical decisions — with the judgment to say no, reshape scope, and protect the team's focus when the expected impact no longer justifies the work.
A pragmatic, business-aligned engineering mindset, a bias for action, and a habit of continuous learning and knowledge sharing.

Responsibilities

Set technical direction — shape platform and domain-level technical strategy that improves developer experience, reliability, security, and cost, and lead the high-complexity, cross-cutting initiatives that deliver it with measurable impact for the organization.
Drive clarity through ambiguity. Take on the most loosely-defined problems, validate the critical assumptions early, and create alignment with stakeholders across teams so others can move quickly and confidently — driving cross-team decisions to a timely close and escalating when needed.
Build the AI-augmented platform. Design org-wide tooling, guardrails, and policy-as-code that help every engineer get more out of AI-assisted development — infra primitives an LLM can safely reason about and PR against, automated review, and standards that hold as AI changes how code gets written.
Own Infrastructure-as-Code standards for Kubernetes, AWS, and GCP using Terraform, Helm, Kustomize, and emerging tooling — setting the patterns other teams adopt and making the platform consumable enough that humans and agents can safely extend it.
Evolve our CI/CD backbone (Argo CD / Workflows / Rollouts, GitHub Actions) into shared, well-reasoned standards that make deploys faster, safer, and easier to reason about across the engineering org.
Instrument and operate. Drive observability with Datadog and Amplitude, set and raise SLOs for the team, own the dashboards, and drive improvements to the shared services and dependencies that move them.
Serve as an escalation point for complex, cross-system P0s, lead incident response, and turn postmortems into durable platform improvements that prevent whole classes of failure.
Identify causes, not symptoms. Find the architectural debt that slows multiple teams and build strategies to address it, and recognize when a system must evolve to meet new requirements, be re-platformed, or be deprecated and removed.
Find and de-risk high-leverage bets — spot the opportunities others miss, write the proposals, get buy-in from stakeholders, and build POCs to de-risk them before committing the team.
Multiply the team. Mentor engineers and remove single points of dependency (including yourself) by simplifying systems others can own, raise the hiring bar and help refine hiring practices, and help the team get more leverage out of AI-assisted development.
Connect the platform to customers and the business by translating customer and product needs into platform investments on the roadmap, and instrumenting the feedback loops that keep the platform aligned with what teams actually need.