Target
Principal Applied Data Scientist - Search and Browse (NLP, Vector Search, LLMs)
$168K–$356K
100 Mathilda Place,Ste 100 Sunnyvale,CA 94086-6017 +1 · Remote or Hybrid · Principal · Full-time · Posted Oct 23, 2026
- Location
- 100 Mathilda Place,Ste 100 Sunnyvale,CA 94086-6017
7000 Target Pkwy N,NCD-0375 Brooklyn Park,MN 55445 - Experience
- 8+ years of industry experience building and scaling ML systems for Search, Recommendation, Personalization, Ads, or AI platforms.
- Education
- PhD or MS in Computer Science, Statistics, Applied Mathematics, Physics, or related quantitative discipline.
- Visa sponsorship
- Work duties cannot be performed outside the country of the primary work location, unless otherwise prescribed by Target.
About the job
Sets technical strategy and leads development of large-scale search, retrieval, ranking, and AI discovery systems for Target’s digital shopping experiences.
What you'll do
- Define the long-term vision and roadmap for Search, Browse, and AI-driven discovery.
- Lead architecture and innovation in retrieval, ranking, semantic search, and generative AI systems, balancing relevance, latency, reliability, and cost.
- Guide cross-functional initiatives, organizational technical direction, experimentation practices, and mentorship across teams.
What they ask for
- 8+ years of industry experience building and scaling ML systems for Search, Recommendation, Personalization, Ads, or AI platforms.
- Strong experience with semantic retrieval, vector search, RAG systems, conversational search, or agentic AI systems.
- Demonstrated Python programming skills and experience defining architecture and strategy for large-scale production AI systems.
- Experience operating large-scale online systems with latency, scalability, reliability, and infrastructure cost requirements.
Pay
$168,000.00 - $356,000.00 USD base salary
Tools
Python, VertexAI, transformers, LLMs, RAG
AI focus
Leads AI-driven search and discovery, including semantic retrieval, conversational search, RAG, agentic AI, external LLM integration, and zero-shot discovery.
Model-written summary · the original posting is authoritative