Staff Applied ML Engineer Full Time NEW

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