Applied AI Science Manager - LLM/VLM for Shopping

Amazon Inc.; Palo Alto (CA); On-site; USD 211,000 - 286,000; Full time

Benefits offered by this job

Health insurance

Restricted stock units (RSUs)

401(k) matching

Paid time off

Parental leave

Job summary

Amazon in Palo Alto is seeking a leader to manage a team working on Large Language Model and Vision-Language Model post-training and alignment for new shopping experiences. You will set the roadmap and vision for science investments across multiple customer experiences to deliver the most helpful AI shopping assistant.

The ideal candidate has deep LLM-based expertise, post-training techniques, and a track record of building teams that ship production AI at scale, collaborating with scientists,

Qualifications

• 2+ years of scientists or ML engineers management experience.
• Knowledge of ML, NLP, Information Retrieval and Analytics.
• 5+ years building ML models or algorithms for business applications.
• Experience delivering complex DL/ML/vision systems to customers.
• Excellent communication with users and cross-functional teams.

Responsibilities

• Manage and grow a team focused on LLM/VLM research and post-training alignment.
• Set roadmaps and long-term vision for multiple customer experiences.
• Bridge customer needs to production AI systems and scalable solutions.
• Collaborate with scientists, engineers, and product managers to ship AI at scale.

Skills

ML/NLP knowledge

Team management

Deep learning systems

Requirements communication

LLM/VLM post-training knowledge

Education

Master's degree in a quantitative field

PhD in engineering/CS/ML or related field

Job description

Amazon in Palo Alto is seeking a leader to manage a team working on Large Language Model and Vision-Language Model post-training and alignment for new shopping experiences. You will set the roadmap and vision for science investments across multiple customer experiences to deliver the most helpful AI shopping assistant.

The ideal candidate has deep LLM-based expertise, post-training techniques, and a track record of building teams that ship production AI at scale, collaborating with scientists,