Senior Software Engineer

Spectro Cloud•San Jose, CA

About The Position

Spectro Cloud, a recognized leader in enterprise Kubernetes management with its flagship Palette platform, is seeking a hands-on Senior Software Engineer to own the resolution of complex, customer-impacting issues in production. This role is the engineering backbone behind customer success, translating field escalations into durable code fixes, backports, and long-term product improvements across commercial and federal deployments. As a Senior Software Engineer, you will operate at the intersection of Support, Product Engineering, and Customer Reliability. You will debug deep into Kubernetes internals, Go microservices, and distributed systems; deliver patches and hotfixes on supported release branches; and drive systemic fixes that prevent recurrence. You will be a technical authority for high-severity escalations and a trusted partner to customers, TAMs, and field engineers. Your core mandate is to keep customer environments stable, supported, and current — while continuously improving the maintainability of the shipping product. Success in this role requires a builder-and-debugger mindset. You should be equally comfortable reading a stack trace, reading Go source, and engaging with a customer. You will use AI responsibly to accelerate triage, log analysis, and reproduction — while maintaining strong engineering accountability for every fix that ships.

Requirements

3+ years of hands-on software engineering experience Engineering, Escalation Engineering, or a comparable production-focused role.
Hands-on experience with Kubernetes - operating, debugging, and modifying Kubernetes and Kubernetes-native components (controllers, operators, CAPI, CRDs) in complex production environments.
Strong development experience - you must be comfortable reading, writing, and shipping production code, not only debugging it.
Proven experience owning high-severity customer escalations end to end, including code-level root cause and fix delivery.
Experience backporting fixes across multiple supported release branches with strong regression discipline.
Experience with debugging skills across networking, storage, and control-plane behavior.
Working knowledge of at least one major cloud platform: AWS, Azure, or GCP.
Excellent written and verbal communication — able to hold technical authority in front of customers and executives.

Nice To Haves

Go (Golang) development experience — building, debugging, and patching Go microservices and Kubernetes-native components.
Certified Kubernetes Administrator (CKA); CKAD or CKS a plus.
Experience with Cluster API (CAPI), controller-runtime, or writing/maintaining Kubernetes operators.
Experience with edge, bare-metal, or virtualization platforms (VMware, KubeVirt, Hyper-V).
Experience delivering CVE remediation and patch releases in a regulated, enterprise SaaS, or federal environment.
Experience with MongoDB, Terraform, Ansible, or CI/CD pipelines (GitHub Actions).
Experience using AI-assisted debugging or engineering workflows with validation and governance controls.

Responsibilities

Own high-severity customer escalations end to end — from initial reproduction through code-level root cause, fix, verification, and customer confirmation.
Debug complex Kubernetes and distributed-systems issues across control plane, cluster lifecycle, networking, storage, and workload runtime.
Produce clear, technically rigorous RCA documents that hold up to customer, field, and executive scrutiny.
Partner with Support, TAMs, and Customer Success to keep customers informed and unblock production impact quickly.
Deliver production-quality fixes in Go across the Palette codebase and Kubernetes-native components.
Backport fixes cleanly across multiple supported release branches with strong regression discipline.
Drive hotfix, patch, and CVE releases through the Software release train, coordinating with QA, Release Engineering, and Product.
Maintain high code-review standards on code changes — small, safe, well-tested, and well-documented.
Build reproduction environments and minimal test cases that convert one-off customer scenarios into permanent engineering assets.
Expand unit, integration, and end-to-end test coverage to prevent regression of every fix that ships.
Partner with QA to harden test suites against the failure modes seen in the field.
Identify systemic gaps in observability, error handling, or upgrade paths and drive them to closure.
Identify recurring escalation patterns and drive engineering changes that eliminate their root causes.
Partner with Product Management and feature teams to feed development insights into roadmap and design reviews.
Improve upgrade, rollback, and day-2 operations based on real-world customer signals.
Contribute to supportability improvements — logs, diagnostics, must-gather tooling, and self-service remediation.
Apply generative AI tools responsibly to accelerate log triage, stack trace analysis, reproduction scaffolding, and RCA drafting.
Use effective prompt-engineering practices to improve the consistency and quality of AI-assisted debugging workflows.
Validate all AI-generated artifacts before use. Increased velocity must never compromise engineering correctness, security, or compliance.
Create and maintain clear, Markdown-based RCAs, fix write-ups, and knowledge-base entries so root cause and remediation intent are documented before implementation.
Own escalations end to end — from customer symptom through code fix, backport, verification, and follow-through on preventative work.
Drive continuous improvement of the software development function through measurable reductions in escalation age, backport lead time, and repeat-defect rate.
Use sustaining KPIs such as time-to-RCA, time-to-fix, backport coverage, escaped-defect rate, and customer-confirmed resolution to guide improvements.
Collaborate effectively across Software Development, Support, Product Engineering, QA, Release Engineering, and Security teams.