Staff Engineer, Yieldmo

• 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
• 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-scale ingestion, streaming and batch pipelines, data modeling, and storage/query optimization
• Experience with distributed data systems (e.g., Kafka, Spark, Flink, Airflow, or equivalents) and modern data lake / warehouse architectures
• ML as a working secondary skill — comfortable integrating embeddings, feature pipelines, and model outputs into data workflows
• Track 3 — Search-leaning
• Hands-on experience designing or building search / retrieval systems — indexing, query processing, ranking, and relevance
• Working knowledge of both classical (inverted index, BM25, learning-to-rank) and modern (dense retrieval, hybrid search, rerankers) approaches
• Practical understanding of the data and ML layers that feed a search system, enough to debug and improve them end-to-end

Desirable

• 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
• You'll 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

• Select candidates will be invited to schedule a 30 minute screening call with a member of our Talent Acquisition team. We will discuss the Hiring Process details at that time. The hiring process typically includes, but is not limited to:
• A 30 minute video interview with the Hiring Manager
• Candidates will be invited to join a remote on-site interview round, consisting of video interviews with various team members and leadership
• Successful candidates will subsequently be made an offer