Staff Applied ML Engineer

Staff Machine Learning Engineer – Search & Recommendations (Bay Area, Hybrid)

A well-funded, high-growth Series A startup is hiring a Staff Machine Learning Engineer to lead advanced search, information retrieval, and recommendation systems powering a core workflow in the construction supply chain.

You’ll be the senior technical lead for applied ML on a small, high-leverage team, working directly with a VP of AI/ML with a deep background in search & recommendations.

What you’ll work on

* Own end-to-end design and implementation of search and recommendation systems over messy, domain-specific, long-form data (RFQs, spec sheets, product catalogs)

* Design and optimize retrieval pipelines (RAG, hybrid search, dense/sparse retrieval) and embedding strategies to achieve high relevance and robustness

* Build and fine-tune models (ranking, retrieval, matching) and iterate using both offline metrics and production feedback

* Make system-level tradeoffs around model architecture, quantization, latency, throughput, and cost for real-time production systems

* Partner closely with product and engineering to turn ambiguous business problems into well-scoped ML projects and production features

* Lay the technical foundations for agentic AI workflows across the full construction RFQ lifecycle

What we’re looking for

* 8+ years experience in applied ML, with a strong track record in:

* Search / information retrieval

* Recommendations / ranking

* Or similar large-scale ML systems in production

* Deep experience with:

* Modern embedding models and vector search

* RAG-style or retrieval-augmented systems

* Relevance tuning, evaluation, and A/B experiments

* Strong coding skills in Python and hands-on experience taking ML systems from prototype to production (you understand the engineering required to ship and maintain models, even if you’re not a pure infra engineer)

* Comfort owning both the science (modeling, evaluation, relevance) and enough of the engineering (APIs, data pipelines, deployment patterns) to work effectively with software teams

* Experience working with unstructured and semi-structured data (text-heavy, noisy, inconsistent formats)

* Ability to provide technical leadership: setting standards, doing design reviews, and mentoring other ML engineers

Nice to have, Experience in:

* Enterprise search, legal/financial/technical document search, or other complex domain search

* Building systems on top of LLMs (fine-tuning, retrieval, prompt engineering, evaluation)

* Quantization / model compression and performance optimization for production workloads

* Prior experience in early-stage (Series A/B) startups or building 0→1 ML products