Applied Scientist II, Natural Language and Multimodal Search

Amazon is hiring an Applied Scientist II, Natural Language and Multimodal Search in Seattle, United States. It pays $143k-$193k a year and Level rates it ; you can apply on Amazon.

Level's read on this job

On Level's scale, this applied scientist role is Builds AI ●●●●: the job is building AI systems.
The posting mentions Python, PyTorch and TensorFlow among its tools and skills.
Level classes it as a mid-level role.
Amazon has 20,000+ other open roles on Level.

AI in this role

Applied Scientist designing and training deep-learning ranking and semantic-matching models for natural-language search.

PythonPyTorchTensorFlow

computer-visionnlpapplied-sciencemachine-learningnatural-language-processingmultimodal-searchdeep-learning

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

How we rate this

Applied Scientist II, Natural Language and Multimodal Search at Amazon rates 95 out of 100 for how much of the daily work is AI. That makes it Builds AI (AI Level 4 of 4). The level is about AI in the job, not seniority.

Classification

Builds AI. The job is building AI systems.

●●●● Builds AI80 to 100
●●●○ Works on AI60 to 79
●●○○ Uses AI40 to 59
●○○○ Little AI0 to 39

Levels come from how often the tools, models and workflows of the role are named in the posting itself. Read the description and count the AI tools, models and workflows it names.

Key facts

Applied Scientist II, Natural Language and Multimodal Search is an open job at Amazon.
Level rates this role AI Level 4 of 4: Builds AI.
The listed salary is $143k-$193k a year.
The role is based in US, WA, Seattle.
The job was posted on July 10, 2026.
Seniority: mid.
Applications are handled by Amazon. Apply on Amazon

Prepare for this job

Built from this posting and from similar jobs on Level.

Skills and AI tools this role asks for

Computer visionNLPApplied ScienceMachine learningNatural Language ProcessingMultimodal SearchDeep learningPython

Also common in applied scientist jobs at this level

LLM Generative AI TensorFlow Spark AWS scikit-learn

Pay

This job starts at: $143k
Typical applied scientist job: $167k

This posting starts 15% below the typical applied scientist job. See applied scientist jobs

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?
Tell me about a project where applied science was part of your work. What did you do?
Tell me about a project where machine learning was part of your work. What did you do?
Tell me about a project where natural language processing was part of your work. What did you do?

Adapt your resume

List these exact terms on your resume: Computer vision, NLP, Applied Science, Machine learning, and Natural Language Processing. 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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