Principal Software Engineer

Sovrn•Boulder, CO

1d•Hybrid

About The Position

Sovrn is a Software and Data business that helps Open Web businesses be and remain independent. We help them understand their business better, operate more efficiently, and make & keep more money. We believe in the freedom and free-flow of information. We believe the Open Web is the largest source of this information. We believe in helping Open Web businesses be and remain Independent. Through Software products and Data solutions we help our customers: Understand their business better , so they can make better decisions Operate their business more efficiently, so they can invest in what matters most Make (and Keep) more money , so they control their own destiny. Sovrn is a supply-side platform (SSP) built to help publishers grow, protect, and understand their businesses. We operate at the intersection of programmatic advertising infrastructure and publisher monetization — processing hundreds of billions of ad requests daily across a global, high-throughput exchange. We're looking for a Principal Software Engineer with deep roots in adtech infrastructure and a genuine conviction about what AI-native engineering looks like in practice. This role is backend-focused at its core — you'll work on the systems that power our exchange, from bid request processing and traffic shaping to data pipelines and auction logic. But we're also at an inflection point in how we build software, and we want this person to help lead us through it. From a generative/agentic AI capabilities standpoint, we already use LLMs and agentic tooling across our data stack and, we’re looking for is someone who can help us take that from general adoption to intentional practice — who has strong opinions about where AI creates real leverage in a high-throughput adtech environment, and who can bring the rest of the engineering organization along with them. Languages/components/tools in our stack: Java, Kafka (Redpanda), AWS, Databricks, Datadog, ONYX.

Requirements

10+ years of software engineering experience, with a strong backend track record
5+ years working specifically in adtech infrastructure — SSP, DSP, exchange, or ad server environments
Deep fluency in the programmatic ecosystem: OpenRTB, bid request/response flows, auction mechanics, supply path optimization, or similar
Experience building and operating systems at exchange scale — high QPS, strict latency budgets, fault tolerance
Demonstrated experience leading AI or agentic engineering efforts in production environments — not just experimentation, but shipped, operated, and iterated on
Hands-on experience with LLM integration patterns: RAG, tool use, multi-step agentic workflows, prompt engineering, and evaluation frameworks
Strong distributed systems fundamentals: messaging, caching, service architecture, observability
Comfort driving technical decisions in ambiguous, fast-moving environments
A point of view on where AI is and isn't the right tool — and the credibility to make that case

Nice To Haves

We understand that no candidate is perfectly qualified for any job. Experience comes in different forms; many skills are transferable; and passion goes a long way. Even more important than your resume is a clear demonstration of accountability and the ability to thrive in a fluid and collaborative environment. We expect you to learn new things in this role and encourage you to apply if your experience is close to what we’re looking for.

Responsibilities

Own the design and evolution of backend systems that operate at exchange scale — high throughput, low latency, always on
Lead architectural decisions across auction infrastructure, bid processing, traffic shaping, and supply-side integrations
Establish and champion AI engineering practices across the team — from prompt engineering and RAG patterns to agentic workflow design and LLM evaluation
Identify high-leverage opportunities to apply AI in our adtech stack: traffic scoring and filtering, bid shading, yield optimization, anomaly detection, and automated decisioning
Set standards for how we evaluate, trust, and operate AI-powered systems in production — including observability, fallback behavior, and model governance
Drive technical standards, design reviews, and engineering best practices across a senior team
Partner with product, data science, and platform teams to ship end-to-end
Help the broader engineering team build fluency and confidence with AI tooling, not just tolerance of it
Mentor and level up engineers through code review, design collaboration, and hands-on guidance