Senior Software Engineer, Backend (Workload Safety)

At Affirm, we exist for the moments that matter—giving people a clear, predictable way to pay over time, with no hidden fees, no surprises, and no tradeoffs on what matters most.

Affirm's engineering team is building a large-scale, highly available, and global infrastructure that is shared across multiple financial products. Ensuring that our infrastructure is accessible to all engineers is critical to the success of the business. We pride ourselves on our culture across engineering of engaging in thorough technical design review, operational excellence, and capable incident response and analysis.

Affirm runs its workloads on more than 85 Amazon EKS clusters, and the number is growing. The Workload Safety team creates one set of standards for how workloads are configured and run, so Affirm can grow globally and still meet its availability and latency SLOs. The team identifies the industry best practices Affirm needs, drives their adoption across every engineering team on EKS, and enforces them through policy and automation so new workloads comply by default.

As a senior engineer on the team, you will help set its roadmap, decide which standards matter most, and build a repeatable way to take each standard from research to enforcement across the fleet.

What you’ll do

Define and evolve workload safety standards for Affirm's EKS fleet, such as CPU and memory settings, workload isolation and placement, tolerance for disruption, graceful termination to name a few

Own each standard end to end: research and prototyping, white-glove onboarding for early adopters, self-service guidance for everyone else, and automation and policy guardrails that keep every workload compliant from day one

Lead cross-team initiatives from design through fleet-wide rollout, mentor other engineers, and set a high bar for design, code review, and operational readiness

Isolate workloads by criticality and resource profile so one misbehaving workload cannot degrade critical paths such as checkout

Fix stuck workloads and node lifecycle problems, such as pods that ignore termination signals, pod disruption budgets that block node cycling, and Karpenter timeouts that prevent bin packing and the removal of excess capacity

Determine the safe operating limits of each cluster (pod count, node count, API server request volume), keep clusters within them, and develop control plane scaling patterns for bursty workloads such as scheduled batch jobs and large deployments

Analyze workload resource profiles across the fleet (CPU, memory, disk, network) to guide EC2 instance type strategy, and automate that analysis as workloads change

Measure each standard's impact on availability, latency, risk, and cost, and use the results to prioritize the roadmap

Participate in on-call for Kubernetes runtime issues across the fleet, and turn recurring failure patterns into roadmap items

Work closely with the teams that run services, batch jobs, and ML workloads on EKS, and with the deployment, capacity, and SRE teams

Keep up with new best practices from the Kubernetes community and adapt them to Affirm's needs

What we look for

5+ years of experience running Kubernetes in production at scale, ideally Amazon EKS across many clusters

Deep understanding of Kubernetes resource management and scheduling: requests and limits, OOM and CPU throttling behavior, affinity and anti-affinity, taints and tolerations, and topology spread constraints

Solid grasp of the pod lifecycle and disruption management, including graceful termination, pod disruption budgets, and node draining

Experience enforcing configuration standards through admission control and policy-as-code (for example, OPA Gatekeeper or Kyverno)

Proficiency in Go or Python, including building production infrastructure software such as Kubernetes controllers, operators, or admission webhooks

Strong production debugging skills and a demonstrated ability to turn recurring incidents into systemic fixes

Track record of leading technical initiatives across multiple teams, driving adoption of shared standards, and mentoring other engineers