AmazonPosted 2mo ago

Applied Scientist II, Natural Language and Multimodal Search

Applied Scientist II, Natural Language and Multimodal Search at Amazon scores 99 out of 100 on AI centrality, which makes it AI Level 4 of 4 (Builds AI) on this board. The level measures how much of the work is AI, not seniority.

US, WA, Seattlemidfull-time$143k-$193k

AI in this role

computer-visionnlp

Amazon Search is reinventing how customers find products through natural-language and semantic understanding. We are looking for an Applied Scientist II to push the science behind Natural Language Search that interprets complex, constraint-rich shopping queries, and retrieves and ranks the most relevant products using both text and product images.

You will build and ship large-scale relevance and ranking models that measurably reduce the rate at which customers see irrelevant results, working on problems that span query understanding, multimodal semantic matching, and contextual ranking at Amazon scale.

Key job responsibilities
- Design, train, and ship deep-learning ranking and semantic-matching models over text and product images that improve search relevance and reduce how often customers see irrelevant results, across hard query types.
- Build the training data and evaluation methods that make these models work: synthetic and historical labels, hard-negative mining, and targeted sampling at the cases where search fails.
- Train and adapt vision-language encoders so product images become a first-class relevance signal, and verify visual constraints that text alone misses.
- Build joint text and image embeddings for retrieval and ranking, including contrastive training, hard-negative mining and fusion of modalities.
- Develop signals that match product attributes to what the customer actually asked for.
- Run offline and online A/B experiments, analyze precision/recall tradeoffs, and iterate to launch.
- Work with engineers and scientists across teams to take models from prototype to production at Amazon scale.

A day in the life
You work alongside scientists and engineers on some of the hardest open problems in search relevance, teaching models to understand what customers really mean when they ask for something specific and nuanced. A typical day blends model development and data curation with sharp experiment analysis: diagnosing where search breaks down for a query segment, designing the fix, and proving the gains through offline metrics and live A/B tests that reach real Amazon customers. The work spans the full range, from surgical fixes that resolve stubborn failure pattern to broad modeling changes that move relevance for millions of queries at once. You'll see your ideas go from whiteboard to production fast, present results regularly to wider team, and help shape the team's relevance roadmap worldwide.

About the team
We are the science team behind Amazon's semantic search relevance and ranking. We own the models that understand nuanced, multi-constraint shopping queries and show products customers actually want. We operate close to production, measure ourselves on real customer-impact metrics, and run a culture of fast, rigorous experimentation. Every model decision is grounded in data.

Basic qualifications

- PhD, or Master's degree and 2+ years of CS, CE, ML or related field experience
- Experience programming in Java, C++, Python or related language
- Experience with one of the following areas: machine learning technologies, Reinforcement Learning, Deep Learning, Computer Vision, Natural Language Processing (NLP) or related applications

Preferred qualifications

- Experience in machine learning, data mining, information retrieval, statistics or natural language processing
- 1+ years of building large-scale machine-learning infrastructure for online recommendation, ads ranking, personalization or search experience
- Experience with A/B testing
- Experience in practical work applying ML to solve complex problems for large scale applications
- Publications in ML, IR, or NLP venues (e.g., NeurIPS, ICML, SIGIR, KDD, ACL)
- Experience training large-scale or deep neural ranking/relevance models
- Experience taking ML models from prototype to production

Amazon is an equal opportunity employer and does not discriminate on the basis of protected veteran status, disability, or other legally protected status.

Our inclusive culture empowers Amazonians to deliver the best results for our customers. If you have a disability and need a workplace accommodation or adjustment during the application and hiring process, including support for the interview or onboarding process, please visit https://amazon.jobs/content/en/how-we-hire/accommodations for more information. If the country/region you’re applying in isn’t listed, please contact your Recruiting Partner.

The base salary range for this position is listed below. Your Amazon package will include sign-on payments and restricted stock units (RSUs). Final compensation will be determined based on factors including experience, qualifications, and location. Amazon also offers comprehensive benefits including health insurance (medical, dental, vision, prescription, Basic Life & AD&D insurance and option for Supplemental life plans, EAP, Mental Health Support, Medical Advice Line, Flexible Spending Accounts, Adoption and Surrogacy Reimbursement coverage), 401(k) matching, paid time off, and parental leave. Learn more about our benefits at https://amazon.jobs/en/benefits.

USA, WA, Seattle - 142,800.00 - 193,200.00 USD annually

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Skills and AI tools this role asks for

Computer VisionNlp

Questions you could be asked

Walk me through a computer vision problem you solved, from raw data to a deployed model.
What NLP problem have you worked on, and how did you measure whether it actually worked?
How would you decide a model or AI system is ready to ship?
Tell me about a time a model underperformed in production. How did you find out, and what did you change?

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List these exact terms on your resume: Computer Vision and Nlp. An applicant tracking system matches the wording, not the idea.
Attach one line of real, concrete experience to at least one of them — a tool named with nothing behind it rarely survives a human read.
Lead with what you built, trained or shipped — this role is judged on the AI system itself, not the tools around it.

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