Who We Are
Hi, We’re Sundogs. We’re using AI and human creative expertise to transform video advertising. Working with the biggest brands in the world, we’re building a way to measure creative effectiveness – helping creators everywhere use AI to tell better, more creative stories, and make their video ads not just better, but revolutionary.
Our team is a global collection of Cannes-winning creatives, ex-Googlers, cutting edge data scientists, and start-up entrepreneurs, and we're poking at the future with curiosity and delight.
We’re looking for a Senior Software Engineer who’s a product-minded, problem solver at heart, and excited to build the next generation of creative intelligence tools.
The Opportunity
We’re looking for a Senior Software Engineer who’s excited to build things from the ground up:
someone who can think strategically, move fast, and care deeply about quality. You’ll be a key player in taking our product from MVP to scalable platform, helping global brands get real results from their creatives.
This is not a narrowly scoped role. You’ll own critical systems end-to-end, and work closely with the data and product teams to translate creative ideas into production-level software. This role is fully remote and candidates from all geographies are welcome to apply.
Please note application instructions. Resumes submitted only via LinkedIn will not be considered.
You’ll Thrive Here if You:
Are energized by ambiguity and enjoy bringing structure where it doesn’t yet exist
Collaborate well with non-technical teammates (creative, product, strategy)
Want to work directly with early adopters who are leading global brands and see your work in the wild, fast
Want to shape not just what we build, but how we build it:
code, culture, process, and technical direction
What You'll Do
Platform & Backend Engineering:
Improve and scale our Ad Scoring API based on client use cases
Design & Build scalable data systems and pipelines for creative and performance signals
Build for scale:
robust, scalable systems with great monitoring, alerting, and failure-handling
AI / LLM Pipeline
Refine our LLM pipelines (OpenAI, Gemini, etc.) for performance and reliability
Collaborate on creative uses of AI across the stack, from scoring to benchmarks
Technical Enablement & Collaboration
Work directly with early API clients to support technical onboarding and create feedback loops to integrate into tech and product roadmaps
Collaborate across teams to make technical work clear and actionable
Bring curiosity and empathy to every interaction—especially when translating technical concepts for non-technical teammates
What We're Looking For
7+ years backend engineering experience, ideally with some experience in fast-paced, high ambiguity environments (start-up or other)
Deep Python experience and fluency and production experience in GCP (or other major cloud provider)
Familiarity with LLMs or data-intensive systems at scale, data engineering experience is a plus
Proven ability to ship scalable, reliable, production-grade systems
Clear, concise, curious and warm communicator, able to make technical concepts simple and accessible
Empathetic collaborator, especially when working with non-technical peers, who thrives on feedback, iteration, and shared success
Our Stack:
Python, FastAPI, Postgres, GCP (Cloud Run, BigQuery, VertexAI), OpenAI, Gemini, SQL
What We Offer
Early equity in a fast-growing start-up
Flexible, remote-first work environment.
Direct access to founding team, clients, and creative challenges
A culture that values curiosity, candor, and craftsmanship
Note:
Our team is fully remote and distributed within EST-CET timezones. While we are distributed across timezones, workdays are local hours (with occasional/rare exceptions).
Application Instructions
Submit your resume and prompt responses to
We're not looking for polish, we're looking for clarity;
we’re interested in your thinking process, not just the technical details. Resumes without answered prompts will not be considered.
In 250 words or less, answer the following question:
Tell us about a time you re-designed or refactored a system (e.G., an API, data pipeline, architecture, or core service). What was the original problem or limitation? What changes did you make and why? What was the result?
In 250 words or less, answer the following question:
Pick a technical system, tool, or project you’ve worked on, like an API, data pipeline, ML model, or cloud architecture, and explain it clearly and simply, as if you were describing it to a teammate in marketing or creative.