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