Applied Scientist II (Bing Places, AI)

TL;DR

Applied Scientist II (Bing Places): Design, build, and ship advanced AI and machine learning solutions spanning LLMs, RAG, learning-to-ranking, and entity understanding to power high-quality local search experiences at scale. Focus on end-to-end model development from problem formulation and data analysis through experimentation, production deployment, and live flighting.

Location: Mountain View, United States

Salary: USD $100,600 – $199,000 per year (USD $131,400 – $215,400 in San Francisco Bay area)

Company

Bing Places team at hirify.global building intelligence for local search experiences used by millions daily.

What you will do

• Formulate complex product and engineering problems as ML and AI tasks and drive them from concept to production.
• Design, implement, and evaluate ML- and LLM-based models to improve Bing Places quality, relevance, and coverage.
• Conduct data analysis to understand system behavior, identify opportunities, and define success metrics.
• Prototype modeling approaches, iterate based on offline evaluation and online A/B experimentation.
• Own experimentation pipelines and partner with engineers to integrate models into production systems.
• Drive technical direction, document results through design reviews, papers, and patents.

Requirements

• Bachelor’s in Statistics, Econometrics, Computer Science, Electrical/Computer Engineering or related AND 2+ years related experience (statistics, predictive analytics, research); OR Master’s AND 1+ year; OR Doctorate or equivalent.
• Experience in machine learning, statistical methods, and data-driven problem solving.
• Hands-on model development and evaluation on large-scale datasets.
• Proficiency in Python and ML frameworks (PyTorch, TensorFlow, JAX).
• Understanding of experimentation including offline metrics and A/B testing.
• Ability to scope problems and collaborate with engineering/product partners.

Nice to have

• Master’s or PhD in relevant field and 4+ years applying AI/LLMs to real-world systems (RAG, ranking, classification).
• Background in search, information retrieval, knowledge graphs, or entity understanding.
• Publications, patents, distributed training, model optimization, production ML infrastructure.

Culture & Benefits

• Collaborative environment working end-to-end on high-impact projects at hirify.global scale.
• Opportunities to contribute to scientific community via publications and patents.
• Comprehensive benefits and compensation; equal opportunity employer.