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Experience in Big Data, search, NLP, and chatbot technologies such as Elasticsearch and Solr. The AI COE is seeking a Senior Data Scientist with experience in Conversational AI, Generative Large Language Models, Natural Language Processing, Machine learning, various deep learning related technologies, predictive and prescriptive analytics.
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To make this vast amount of information more accessible, we have built a variety of tools including a search engine, a machine-learning-driven search intent classifier, an LLM-driven question -answering system and an ontology/ taxonomy all supported by reference data about companies, industries, and our analysts.
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Responsible for the design and development of custom ML, Gen AI, NLP, LLM Models for batch and stream processing-based AI ML pipelines including data ingestion, preprocessing modules, search and retrieval, Retrieval Augmented Generation (RAG), NLP/LLM model development and ensure the end-to-end solution meets all technical and business requirements, and SLA specifications.
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Strong background in AI, machine learning and LLMs Experience in NLP techniques Familiarity with programming languages used in the LLM ecosystems (Python) Required Skills: Strong background in AI, machine learning and LLMs Experience in NLP techniques Familiarity with programming languages used in the LLM ecosystems (Python.
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Based on the specific data science team, this role would need to be Proficient in one or more data science specializations, such as optimization, computer vision, recommendation, search or NLP.
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Participate in the entire life cycle of ML/NLP projects from inception to deployment including and up to necessary support and post deployment validations full implement best practices in data science and machine learning.
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Data science, machine learning, optimization models, PhD in Machine Learning, Computer Science, Information Technology, Operations Research, Statistics, Applied Mathematics, Econometrics, Successful completion of one or more assessments in Python, Spark, Scala, or R, Supervisory experience, Using open source frameworks (for example, scikit learn, tensorflow, torch.
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Experience with one or more of the following: Machine Learning, Deep Learning, NLP, LLM, ranking systems, recommendation systems. - Encompass the development and fine-tuning of NLP strategies like query understanding, semantic mining, LLM algorithms, knowledge graph, and multi-model text/photo/video comprehension, etc.
$112,065 - $172,800 a yearFull-timeExpandApply NowActive JobUpdated Today - UpvoteDownvoteShare Job
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The Figure Eight platform transforms audio, video, text, and images into high-quality annotated data to support a variety of use cases ranging from computer vision and search relevance to data categorization and natural language processing (NLP.
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Familiar with 1-2 areas in natural language processing, computer vision, multimodal, graph algorithms, search algorithms, text/data mining, and LLM. 2+ years of experience with solid foundation in data structures/algorithms, proficient in machine learning/deep learning theory, and rich practical experience.
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We use the best machine learning techniques to simplify product discovery for animal lovers looking for their pet needs on Chewy.com. Our outstanding multi-disciplinary team of data scientists, data engineers, software/front-end engineers, and product managers work together to power personalized recommendations and search for pet parents.
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Good understanding of data science and machine learning fundamentals like regression, classification, tree-based approach, neural networks, and sequence-based models. Collaborate with cross-functional teams to understand business requirements and develop scalable, efficient machine learning solutions that improve content onboarding, search, recommendations, and voice interactions.
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Together, Appen and Figure Eight Federal, combine the best of human and machine intelligence to provide high-quality annotated training data that powers the world’s most innovative machine learning (ML) mission solutions.
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Nothing moves in AI and machine learning (ML) without training data, and only high-quality training data can produce accurate and robust AI/ML powered solutions. Across Defense, Health and Human Services, Homeland Security and Emergency Management, Sciences, Natural Resources and Regulatory functions- all are recognizing the power of human-machine teaming.
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This role requires proficiency in search engines, natural language processing (NLP), machine learning and statistical techniques. Analyze data and documents from enterprise applications and other data sources to support knowledge management capabilities of search & discovery and information retrieval.
$137,500 - $224,600 a yearFull-timeExpandApply NowActive JobUpdated Today
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Whether you’re applying for your first job or looking to move up the career ladder, personality tests aren’t usually the first thing we think about. But surprisingly, they can have a massive impact on how our future employers perceive us. In fact, a 2017 study by the Society for Human Resource Management (SHRM) has found that 32% of U.S. employers use personality tests when hiring for senior management positions, and 28% use them for middle management positions. Personality tests are also used for hourly workers and contractors, though less frequently.
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With the advent of desktop computers, the arduous task of scouring through weekly job classifieds became a thing of the past. The mid-1990s brought about a new era where job seekers could easily search and apply for jobs online. The introduction of AOL's Instant Messaging feature provided an even faster means for employers and candidates to communicate and schedule interviews. As smartphones became more pervasive in the early 2000s, hiring managers increasingly used phone calls for screening and interviewing candidates. Despite this trend, over 80% of interviews still took place in person.
A Potential TikTok Ban?!
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The Effects of Workplace Racism and Sexism
One day it's a covert statement to a mother returning to work after maternity leave. Another day it's a lingering gaze at an employee enjoying a culturally rich meal. These microaggressions (or sometimes macroaggressions) can take an employee from a confident, high-performer to one that feels insecure being themselves at work. Your employees engage with people with different ideas and feel most comfortable and valued when they can work without losing their cultural, racial, and gender identity. While most employers know this, why have workplace racism and sexism often been neglected?
When Rage Applying Strikes: How to Identify Unserious Candidates
As the job market remains highly competitive, we have seen a surge in "rage applying." This is when candidates apply to multiple jobs, often without considering whether they are truly interested in the role. Rage applying goes hand-in-hand with quiet quitting. Often, employees want to entertain the thoughts and feelings of leaving their job, but they aren't necessarily serious about leaving yet. Meanwhile, other employees engaging in this trend are actually trying to find a better role. As a recruiter, it can be hard to identify who are the real applicants in a sea full of quiet quitters, but understanding rage applying and identifying red flags will certainly help.
How to Increase Job Ad Exposure
In today's competitive job market, writing quality job ads is critical for attracting top talent to your organization. While networking and candidate referrals are prime real estate for finding qualified candidates, nothing beats the tried-and-true method of writing an extraordinary job ad. But while writing a great job ad is the first step, what's more important is increasing visibility. You could have the most detailed, well-written ad on the internet, but if no one sees it, then you are wasting time (and potentially money!). Employers often believe that job boards are the root of the problem, but you can learn how to increase job ad exposure by tweaking a few steps of your recruitment process.