Senior Software Engineer, Back End & Infrastructure

Evertune•Seattle, WA

11h•Hybrid

About The Position

Evertune is building the first AI discovery platform for modern marketers. As large language models (LLMs) become the go-to source for recommendations – we help brands understand exactly what AI is saying about them, where they stand against competitors, and how to show up more often in AI-powered answers. Our platform turns black-box AI behavior into actionable insights, so marketing teams can make faster, smarter decisions that drive growth. Founded by leaders who helped scale The Trade Desk into the world’s leading ad tech platform, we bring deep expertise in digital advertising, data, and high-growth environments. Now, we’re pioneering a new category at the intersection of AI, SEO, and brand strategy. You’ll be joining Evertune’s software engineering team– a tight-knit, high-velocity group, with most of us based in Seattle and New York. While we span time zones, we operate with tight alignment and shared urgency. Our engineers optimize for high velocity: we ship fast, sweat the details, and genuinely enjoy building together. As a foundational hire, you'll be helping to shape not just how we code – but how we grow, communicate, and scale. You’ll take and share deep technical ownership of the backend systems that power our AI Discovery platform, working at the bleeding edge of evaluating and applying LLMs to real-world problems. This includes designing distributed systems, shaping our cloud and infrastructure patterns, optimizing AI integrations, and making architectural decisions that help us scale quickly and reliably. You are a software engineer first – someone who understands how to build software systems with strong engineering discipline – but you’re also excited to operate at the infrastructure layer. You understand how a strong and stable infrastructure benefits the entire organization. You can reason about cloud environments, permissions, deployment systems, and performance bottlenecks, and you know how to simplify high-ambiguity systems into clean, reliable abstractions.

Requirements

7+ years building and owning resilient, high-performance backend systems for user-facing or B2B products
Strong software engineering discipline - you design testable systems, write meaningful automated tests, and protect against regressions as systems evolve
Deep proficiency in Python (or similar) and a track record of designing distributed systems
Operated production workloads in cloud environments (GCP, AWS, Azure, or similar), including IAM, service accounts, access controls, and environment isolation
Improved the performance and reliability of ambiguous or legacy systems by simplifying architecture and reducing operational risk
Built and maintained infrastructure as code using Terraform or equivalent
Designed and deployed containerized services using Docker and modern CI/CD workflows
Designed scalable data models and worked deeply with databases such as MongoDB or similar
Integrated and operated third-party APIs at scale with attention to performance, cost, and failure handling
Worked with observability tooling to monitor, debug, and improve production systems
Strong communication skills and the ability to influence technical direction
Actively leverage LLMs or AI tooling to improve engineering workflows
Must be able to work in our Seattle or NYC office 3 days per week (hybrid model)

Nice To Haves

Experience operating systems across multiple cloud providers
Has built or supported generative AI, LLM, or ML-powered systems
Experience designing event-driven systems using queues or streaming infrastructure
Previous or current experience in startup or high-growth environments
Proficiency in utilizing agentic coding tools (e.g., Claude Code).

Responsibilities

Architect, build, and maintain scalable backend services in Python and Go
Design resilient APIs, optimize data pipelines, and improve system performance and reliability
Own and evolve parts of our cloud infrastructure, including environment configuration and access patterns
Implement and manage infrastructure as code (Terraform or equivalent)
Design and maintain containerized services using Docker and modern deployment workflows
Integrate and test data from third-party APIs, including LLM providers like OpenAI and Anthropic
Manage performance, cost, and observability across AI integrations and backend systems
Debug and troubleshoot issues across application and infrastructure layers
Simplify and scale complex systems to support product growth and increasing data volume
Influence long-term technical direction and backend architecture
Mentor junior engineers through code reviews and coaching
Collaborate across the stack to ship product features quickly and reliably