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Nlp Semantic Search Jobs (NOW HIRING)

Amazon

Applied Scientist II, Amazon Search

Seattle, WA · On-site

Amazon Search is reinventing how customers find products through natural-language and semantic ... NLP) or related applications PREFERRED QUALIFICATIONS - Experience in machine learning, data mining ...

Machinify

Staff Data Scientist | NLP

$180K - $230K/yr

Design and train NLP models for tasks like classification, entity extraction, retrieval, summarization, and semantic search * Fine-tune and evaluate LLMs (open-source and API-based); build RAG ...

Accelint

Machine Learning Engineer

Design, develop, and maintain NLP pipelines for technical and structured document understanding, including information extraction, summarization, semantic search, and question answering. * Build and ...

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Nlp Semantic Search information

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$83.5K

$127K

$171K

How much do nlp semantic search jobs pay per year?

As of Sep 11, 2026, the average yearly pay for nlp semantic search in the United States is $127,031.00, according to ZipRecruiter salary data. Most workers in this role earn between $109,000.00 and $143,500.00 per year, depending on experience, location, and employer.

What is NLP semantic search?

NLP Semantic Search is a technology that uses Natural Language Processing (NLP) to understand the meaning and context of search queries and documents, rather than relying solely on exact keyword matches. This allows search systems to provide more relevant and accurate results by interpreting synonyms, related concepts, and user intent. NLP Semantic Search is commonly used in modern search engines, chatbots, and enterprise knowledge management systems to improve information retrieval and user experience.

What skills and qualifications are needed to thrive as an NLP semantic search engineer?

To thrive as an NLP Semantic Search Engineer, you need a solid background in computer science, natural language processing, and information retrieval, often supported by a degree in a related field. Familiarity with programming languages like Python, deep learning frameworks such as TensorFlow or PyTorch, and experience with search platforms like Elasticsearch are typically required. Strong analytical thinking, problem-solving abilities, and effective collaboration skills help you design and refine semantic search solutions. These competencies are crucial for building accurate, efficient systems that deliver relevant search results and enhance user experience.

How does an NLP semantic search specialist typically collaborate with data engineers and product teams?

An NLP Semantic Search specialist regularly works with data engineers to ensure access to high-quality, well-structured data necessary for building and refining search models. Collaboration with product teams is also essential, as they help define user requirements and success metrics for semantic search features. This multidisciplinary teamwork helps align technical solutions with business objectives, often involving brainstorming sessions, iterative feedback on prototypes, and ongoing evaluation of search relevance and performance. Clear communication and adaptability are key for seamless integration across these roles.

What other helpful pages are available for Nlp Semantic Search?

Other pages related to Nlp Semantic Search:

Infographic showing various Nlp Semantic Search job openings in the United States as of September 2026, with employment types broken down into 1% Locum Tenens, 76% Full Time, 18% Part Time, and 5% Contract. Highlights an 91% Physical, 2% Hybrid, and 7% Remote job distribution, with an average salary of $127,031 per year, or $61.1 per hour.

Machine Learning Engineer, Local Search & Marketplace

Mountain View, CA • On-site

NewsBreak
See more jobs at NewsBreakexternal

$190K - $300K/yr

Full-time

Re-posted 20 days ago

Job description

About the Role

We are looking for a Machine Learning Engineer to build intelligent systems that connect consumers with relevant local information, businesses, and services.

You will work across areas such as search, recommendation, ranking, retrieval, user-intent understanding, personalization, and marketplace matching. Depending on your background and interests, you may focus on understanding consumer demand, improving search and recommendation relevance, or building models that more effectively match users with local supply.

This is a hands-on role with the opportunity to take machine learning solutions from problem definition and experimentation through production deployment and iteration. We welcome candidates across a range of experience levels, and the scope and level of the role will be calibrated based on the candidate's experience.

What You'll Do
  • Build and improve machine learning models for search, recommendation, ranking, retrieval, matching, and personalization.
  • Develop systems that understand user queries, behaviors, preferences, and context.
  • Apply embeddings, natural language processing, large language models, and modern retrieval techniques to connect consumer demand with relevant local content, businesses, or services.
  • Build and optimize end-to-end ML pipelines, from data preparation and model training to online serving and monitoring.
  • Partner with product, engineering, and data teams to define problems, identify opportunities, and translate business needs into scalable ML solutions.
  • Design and analyze online and offline experiments to measure model quality and product impact.
  • Improve key product outcomes such as relevance, engagement, conversion, retention, and marketplace efficiency.
  • Explore new ML and LLM techniques and bring them into production where they can deliver measurable value.
What We're Looking For
  • Experience building machine learning, data mining, search, recommendation, ranking, NLP, or related systems through academic projects, internships, or industry work.
  • Strong programming skills in Python, Java, C++, or another relevant language.
  • Solid understanding of machine learning fundamentals, data structures, algorithms, and statistical analysis.
  • Experience with one or more of the following:
    • Search, retrieval, or learning-to-rank
    • Recommendation or personalization
    • Query understanding, intent classification, or NLP
    • Embeddings, semantic search, or large language models
    • Matching, marketplace optimization, or dispatch
  • Ability to work with large-scale or complex datasets and translate ambiguous problems into practical solutions.
  • Strong collaboration and communication skills.
  • Interest in building production systems that serve real users and deliver measurable product impact.
Nice to Have
  • Experience deploying and maintaining machine learning models in production.
  • Experience with experimentation, including A/B testing, causal analysis, or marketplace experiments.
  • Familiarity with deep learning frameworks and ML infrastructure, such as PyTorch, TensorFlow, Spark, Kubernetes, or feature and model-serving platforms.
  • Experience building taxonomies, user-interest representations, knowledge graphs, or behavioral models.
  • Experience applying LLMs to search, recommendation, classification, or information retrieval.
  • Background in local search, local services, maps, commerce, marketplaces, delivery, mobility, or location-based products.
  • Experience at a consumer internet, search, recommendation, advertising, e-commerce, or marketplace company.
Annual Base Pay Range

$190,000 - $300,000

The US base salary range for this full-time position will vary based on job-related skills, level, experience, geographic location, and relevant education or training. 

At NewsBreak, we design our overall rewards package to attract top talent. Depending on the position and level, the role may also be eligible for a discretionary bonus and equity. Your recruiter can share more details during the hiring process.

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