Senior Software Engineer, Platform

Multiplier Technologies

19h•Remote

About The Position

As a Senior Software Engineer in Platform Engineering, you will build internal platforms and AI-enabled tooling that make shipping high-quality software the default. You will contribute to key parts of the Platform roadmap — from problem discovery to rollout and adoption. You will build developer productivity tools/frameworks that scale quality across teams (not gatekeeping). Design and implement CI quality gates (unit/integration/API/contract/E2E), test reliability improvements, and quality signals that prevent production issues. Build platforms for automation utilities, reporting, and observability to improve confidence and reduce engineering toil. Embed AI-assisted workflows into processes (e.g., test generation, test data generation, failure analysis, flake detection, regression recommendations, smarter triage). Partner closely with Product Engineering, SRE/Platform, and Security teams to improve release confidence, reduce regressions, and shorten feedback loops. Set standards and build reusable libraries/SDKs so teams can easily "do the right thing" with minimal friction. Automate everything that an engineer should not be working on.

Requirements

5+ years of industry experience as a software engineer, preferably in SaaS / product engineering.
Proven ability to take ownership, drive initiatives, and deliver platforms adopted by multiple teams.
Strong software engineering fundamentals: system design, APIs, code quality, scalability, and reliability.
Deep proficiency in at least one high-level language (e.g., Java, JavaScript/TypeScript, Go, Python).
Strong understanding of modern engineering practices: CI/CD, developer workflows, branching/release strategies, PR checks, build pipelines.
Experience with cloud infrastructure (AWS / GCP) and distributed systems concepts.
Strong problem-solving skills, comfort working with complex systems, and a bias toward automation.
Hands-on experience with AI (must-have): Using LLMs/AI agents in engineering workflows
Building AI-powered tooling (prompting, evaluation, guardrails, automation integration)
Practical experience integrating AI into developer productivity platforms.
Highly data-driven mindset — comfortable defining success metrics, measuring adoption, and iterating.

Nice To Haves

Experience building/maintaining test automation frameworks (API/contract/E2E), test infra, or quality tooling used org-wide.
Exposure to microservices architecture, GraphQL/REST, event-driven systems, and contract testing.
Experience improving test reliability (flake reduction), failure triage automation, test impact analysis, or regression risk scoring.
Familiarity with observability tools (logs/metrics/traces) and building actionable quality dashboards.
Experience building internal platforms/SDKs and driving adoption through documentation, enablement, and DX improvements.

Responsibilities

Contribute to key parts of the Platform roadmap — from problem discovery to rollout and adoption.
Build developer productivity tools/frameworks that scale quality across teams (not gatekeeping).
Design and implement CI quality gates (unit/integration/API/contract/E2E), test reliability improvements, and quality signals that prevent production issues.
Build platforms for automation utilities, reporting, and observability to improve confidence and reduce engineering toil.
Embed AI-assisted workflows into processes (e.g., test generation, test data generation, failure analysis, flake detection, regression recommendations, smarter triage).
Partner closely with Product Engineering, SRE/Platform, and Security teams to improve release confidence, reduce regressions, and shorten feedback loops.
Set standards and build reusable libraries/SDKs so teams can easily "do the right thing" with minimal friction.
Automate everything that an engineer should not be working on.