Staff Engineer, AI & Search

Who We Are

Yieldmo is an advertising platform that helps brands invent creative experiences through tech and AI, using custom ad formats, proprietary attention signals, predictive format selection, and privacy-safe premium inventory curation. Yieldmo believes all ads should be human-centered, tailored, and provoke users' emotions and actions. Yieldmo helps brands deliver the best ad for every impression opportunity, merging creative and media for proven results. 

 

What We Need

We’re building a general-purpose, AI-powered search engine that will redefine how users discover and engage with content across major publishers. We’re looking for engineers to join the team building it — people who want hands-on ownership of real problems in retrieval, ranking, data, and ML infrastructure at scale.

This is a generalist role, and we’re open to strong candidates from multiple backgrounds. We are hiring across a range of seniority levels (mid-senior through staff) and are specifically interested in engineers who fit one of the following profiles:

• ML-leaning engineer: Strong machine learning foundations with solid applied / backend engineering skills — you’ve shipped ML systems into production, not just notebooks.
• Data / ingestion-leaning engineer: Strong data engineering and large-scale ingestion background, with ML as a working secondary skill — you’re comfortable picking up models, embeddings, and evaluation pipelines.
• Search-leaning engineer: Strong search engineering with working, hands-on understanding of data, ML, and ingestion — you’ve built or meaningfully contributed to real search or retrieval systems end-to-end.

Across all three paths, we care most about builders — engineers who write code, iterate quickly, make pragmatic tradeoffs, and raise the bar for the people around them.

 

What You Can Expect In This Role

• Design, build, and operate core components of Yieldmo’s AI-driven search engine — retrieval, ranking, indexing, ingestion, or ML infrastructure, depending on your strengths.
• Be a hands-on builder: writing production code, iterating quickly, and owning systems from prototype through scale.
• Partner closely with Product, ML, and Engineering teams to integrate modern retrieval, ranking, and recommendation technologies (LLMs, embeddings, vector search, RAG).
• Contribute to the technical direction of the search platform and influence architectural decisions within your area.
• Build and operate large-scale data and content ingestion pipelines that feed the search system.
• Drive quality, performance, relevance, and reliability bars for the features and services you own.
• Mentor peers and, for more senior candidates, grow into tech-lead responsibilities as the team scales.

 

Requirements

We expect every candidate to meet the core bar below, plus go deep in at least one of the three specialty tracks that follow.

Core

• Strong software engineering fundamentals and production experience building and operating backend systems at scale.
• Proficiency in Python and SQL; comfort with Docker and microservices architectures.
• Working familiarity with modern AI/search building blocks: LLMs, embeddings, vector databases, retrieval-augmented generation (RAG), function/tool calling.
• Ability to work cross-functionally in a fast-moving environment, with excellent written and verbal communication.
• A hands-on, ownership-oriented mindset — you ship.

Track 1 — ML-leaning

• Strong ML foundations: ranking/relevance, embeddings, representation learning, or LLM fine-tuning and evaluation.
• Proven track record shipping ML systems to production, including training pipelines, model serving, and online/offline evaluation.
• Solid applied engineering: you can own the backend and infra around your models, not just the modeling.

Track 2 — Data / ingestion-leaning

• Strong data engineering background: large-s