Staff Engineer, AI & Search
About The Position
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.
Requirements
- 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.
- 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.
- 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.
- 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.
Nice To Haves
- Exposure to or direct experience at leading AI/search organizations (OpenAI, Anthropic, Perplexity, xAI, Google DeepMind, etc.).
- Experience with publisher-scale content, recommendation systems, or adtech.
Responsibilities
- 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.
Benefits
- Remote Work: Our team is fully distributed, though we love an opportunity to get together at our annual offsites, holiday parties, and more.
- 100% Company Paid Health Coverage: Choose the medical, dental, and vision plan that’s best for you and your family – all with options for 100% company paid coverage.
- 401(k) Plan: Invest in yourself by participating in our 401(k) plan with a company match.
- Equity: Share in Yieldmo’s success through our employee stock option program.
- Flexible Time Off, Company Slowdowns, and Summer Fridays: Take time off to relax and rejuvenate on your own terms with flexible time off, multiple company slowdowns, and Summer Fridays.
- Home Office Setup and Stipend: Setup your home office for success with our premium technology packages and an additional stipend for any extra needs.
- Professional Development: Grow your hard and soft skills with our annual professional development stipend.
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What This Job Offers
Job Type
Full-time
Career Level
Mid Level
Education Level
No Education Listed
Number of Employees
11-50 employees
