Staff Applied AI Engineer | Bay Area (Hybrid) | $200k–$298k base + equity
I'm partnering with a high-growth SaaS company at the cutting edge of AI and compliance on a senior Applied AI Engineer hire.
This is not a typical "build a model and ship it" role.
This is where you define what good AI looks like — owning how systems retrieve, reason, and deliver trustworthy outputs at scale.
You'll sit at the intersection of research and real-world impact, shaping the intelligence behind core product features.
• Improving RAG systems — retrieval quality, chunking, embeddings, hybrid search
• Designing evaluation frameworks (metrics, golden datasets, regression detection)
• Building and tuning ranking + reranking systems (cross-encoders, LLM rerankers)
• Running experiments to validate what actually improves performance
• Debugging failure modes across retrieval, reasoning, and generation
• Prototyping agent-style workflows over complex, document-heavy data
• Exploring ML approaches beyond GenAI (ranking, classification, probabilistic models)
• 8–10+ years in applied ML, data science, or AI research
• Strong experience in information retrieval / search relevance
• Hands-on with RAG systems and retrieval optimization
• Deep understanding of evaluation + experimentation (A/B testing, metrics)
• Python + strong problem-solving / research mindset
• Someone who can explain why systems work (or don't) — not just build them
• You've only used LLM APIs without optimizing retrieval or evaluation
• Your experience is purely prompt engineering
• You prefer purely academic research without product impact
• You're looking for a heavily structured, slow-moving environment
Candidates must be based in the Bay Area and open to a hybrid setup
If you're interested in making AI systems measurably better — not just building them, drop me a message or comment below.
Happy to share more details confidentially.
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