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Natural Language Processing Research Jobs (NOW HIRING)

Research Intern

$20 - $30/hr

The Research Intern will assist in the curation of medical imaging and clinical report data and the ... and natural language processing of clinical reports * Document code, workflows, analysis ...

We are seeking a Research Engineer who willbringexpertiseinAI and ML andisinterestedinbuilding data ... Previousexposure to Natural Language Processing (NLP) problems andhavefamiliarity with key tasks ...

We are seeking a Research Engineer who willbringexpertiseinAI and ML andisinterestedinbuilding data ... Previousexposure to Natural Language Processing (NLP) problems andhavefamiliarity with key tasks ...

We are seeking a Research Engineer who willbringexpertiseinAI and ML andisinterestedinbuilding data ... Previousexposure to Natural Language Processing (NLP) problems andhavefamiliarity with key tasks ...

We are seeking a Research Engineer who willbringexpertiseinAI and ML andisinterestedinbuilding data ... Previousexposure to Natural Language Processing (NLP) problems andhavefamiliarity with key tasks ...

... in Natural Language Processing and Generative AI. The role involves implementing scalable AI ... Research latest related academic papers and test nascent ideas which can potentially solve new ...

Research experience in deep learning, reinforcement learning, natural language processing, computer ... vision, recommendations, ranking, search, or related areas * Programming experience in Python and ...

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Natural Language Processing Research information

What are the key skills and qualifications needed to thrive as a Natural Language Processing (NLP) Researcher, and why are they important?

To thrive as a Natural Language Processing Researcher, you need a solid background in computer science, machine learning, linguistics, and typically a graduate degree in a relevant field. Proficiency with programming languages like Python, deep learning frameworks (such as TensorFlow or PyTorch), and NLP libraries (like NLTK or spaCy) is essential, along with experience in publishing academic research. Strong analytical thinking, creativity, and effective communication skills help in developing novel solutions and collaborating within interdisciplinary teams. These skills are vital for advancing the field, solving complex language problems, and driving impactful research outcomes.

Is NLP a good career?

Natural Language Processing (NLP) is a growing field within artificial intelligence that involves developing algorithms to understand and generate human language. Careers in NLP typically require skills in machine learning, programming, and linguistics, and offer opportunities in tech companies, research institutions, and startups. The demand for NLP expertise is increasing as businesses seek to improve automation, chatbots, and language translation tools.

What are some typical challenges faced by professionals in Natural Language Processing (NLP) research roles?

NLP researchers often encounter challenges related to the complexity and ambiguity of human language, such as handling sarcasm, idioms, or multilingual datasets. Keeping up with rapid advancements in deep learning architectures and large language models is also a common demand. Additionally, working with large-scale datasets requires robust data cleaning and preprocessing skills, as well as collaboration with cross-functional teams like data engineers and product managers to ensure research findings translate into practical applications.

Will AI replace NLP?

As a Natural Language Processing researcher, AI is a tool that enhances NLP capabilities rather than replacing the field. Advances in machine learning and deep learning continue to improve NLP applications, but human expertise remains essential for developing, training, and evaluating these systems. The role involves staying current with evolving algorithms and tools to ensure effective language understanding and processing.

What is Natural Language Processing (NLP) research?

Natural Language Processing (NLP) research focuses on enabling computers to understand, interpret, and generate human language. Researchers in this field work on developing algorithms and models that help machines process text and speech, such as chatbots, translation systems, and sentiment analysis tools. NLP research combines knowledge from linguistics, computer science, and artificial intelligence to solve complex language-related problems. Common tasks include language modeling, machine translation, and information extraction.

What is the difference between Natural Language Processing Research vs Data Scientist?

AspectNatural Language Processing ResearchData Scientist
Required CredentialsAdvanced degrees in CS, NLP, or AI; research experienceDegree in CS, Statistics, or related; some research experience beneficial
Work EnvironmentResearch labs, academia, R&D departmentsBusiness environments, tech companies, analytics teams
Employer & Industry UsageUniversities, research institutions, tech companies focusing on NLPVarious industries including finance, healthcare, tech, e-commerce
Common Search & Comparison IntentUnderstanding research roles in NLPUnderstanding data analysis and modeling roles

Natural Language Processing Research focuses on developing new algorithms and models to advance NLP technology, often within academic or research settings. Data Scientists analyze data to extract insights, build predictive models, and support business decisions. While both roles require strong analytical skills, NLP Research emphasizes innovation in language models, whereas Data Scientists focus on applying data techniques across various domains.

Is NLP a dead field?

Natural Language Processing (NLP) is an active and rapidly evolving field within artificial intelligence, with ongoing research and commercial applications such as chatbots, translation, and sentiment analysis. NLP specialists are in demand as organizations seek to improve human-computer interaction using machine learning tools and large datasets. The field continues to grow with advancements in deep learning and transformer models like GPT and BERT.

Is ML a high paying job?

Machine Learning (ML) roles, including those in Natural Language Processing research, are generally well-compensated due to the specialized skills required, such as programming, data analysis, and knowledge of algorithms. Salaries vary based on experience, location, and industry, but advanced ML positions often offer above-average salaries compared to many other tech roles.
More about Natural Language Processing Research jobs
Infographic showing various Natural Language Processing Research job openings in the United States as of June 2026, with employment types broken down into 77% Full Time, 20% Part Time, and 3% Contract. Highlights an 89% Physical, 3% Hybrid, and 8% Remote job distribution.
Research Scientist / Engineer, Foundation Model Evaluation

Research Scientist / Engineer, Foundation Model Evaluation

Apple

Cupertino, CA

$181K - $318K/yr

Full-time

Medical, Dental, Retirement

Posted yesterday


Apple rating

8.1

Company rating: 8.1 out of 10

Based on 662 frontline employees who took The Breakroom Quiz

6th of 30 rated technology retailers


Job description

We build frontier foundation models that power intelligent experiences at Apple. Our team works across the full training lifecycle: including pre-training foundation models, and developing mid-training approaches that bridge general capability and task-specific performance. What makes our work distinct is that we're engineering models specifically for Apple silicon and optimized for experiences that are private, personal, and deeply integrated into the OS. We're solving frontier problems in reward modeling to resist reward hacking, handling sparse and delayed rewards in agentic settings, and aligning models reliably across the spectrum from open-ended creative tasks to precise, action-taking workflows. If you're drawn to hard problems where the research and the product are inseparable, this is the team.
Description
This is a hands-on role focused on the models that power Apple products used daily by over a
billion people. You will design evaluation systems where the outcome is not just a score, but an
actionable signal - one that drives model improvement and predicts real user experience.
Working alongside model training and product teams, you will close the loop between evaluation
and improvement.
Our work spans three areas:
• Frontier capability assessment: benchmarking against the state of the art in reasoning,
code, knowledge, and agentic workflows
• Product-aligned evaluation: measuring model quality in ways that reflect real user
experience
• Evaluation-to-training integration: feeding actionable insights back into the model
development cycle
You may focus on one area or work across multiple, depending on your background and
interests.
We build frontier foundation models that power intelligent experiences at Apple. Our team works across the full training lifecycle: including pre-training foundation models, and developing mid-training approaches that bridge general capability and task-specific performance. What makes our work distinct is that we're engineering models specifically for Apple silicon and optimized for experiences that are private, personal, and deeply integrated into the OS. We're solving frontier problems in reward modeling to resist reward hacking, handling sparse and delayed rewards in agentic settings, and aligning models reliably across the spectrum from open-ended creative tasks to precise, action-taking workflows. If you're drawn to hard problems where the research and the product are inseparable, this is the team.","responsibilities":"Benchmark Design & Development: Design and implement evaluation benchmarks, metrics, and test suites that rigorously measure model capabilities across reasoning, knowledge, code, and agentic workflows.
Product-Aligned Evaluation: Develop evaluation methods that capture how models behave in real product settings, and validate that evaluation metrics predict user-perceived quality and product outcomes.
Evaluation Methodology Research & Tooling: Research and apply state-of-the-art evaluation techniques - including scoring frameworks, model-based judging, and contamination-resistant benchmark design. Build reusable tools, scorer libraries, and analysis frameworks that scale across the team's benchmark portfolio.
Experimental Analysis: Design and execute rigorous experiments comparing model capabilities, engage with third-party vendors on benchmarking, and perform detailed gap analysis to guide model development priorities.
Cross-Team Collaboration: Work closely with model training, training data, and product teams to ensure evaluation insights inform training strategies, data decisions, and product quality improvements.
Preferred Qualifications
PhD in Computer Science, Machine Learning, NLP, or a related field
Direct experience evaluating large language models, e.g. benchmark design, model-based judging
Track record of collaborating with model training and data teams to turn evaluation findings into training improvements
Experience building reusable evaluation tooling or analysis frameworks adopted across teams
Familiarity with human evaluation methodology and experience partnering with annotation teams or vendors to assess model quality
Minimum Qualifications
3+ years of experience in AI model evaluation, NLP, or a related area (e.g., natural language generation, information retrieval, or conversational AI)
Strong fundamentals in machine learning, natural language processing, and statistical analysis
Proficiency in Python and experience with ML frameworks (PyTorch, JAX, or equivalent)
Demonstrated ability to translate research insights into practical implementations
Strong experimental design skills: ability to design rigorous comparisons and draw valid conclusions from results
Clear technical communication: ability to distill evaluation results into actionable recommendations for cross-functional partners
MS or PhD in Computer Science, Machine Learning, Natural Language Processing or a related technical field. Equivalent practical experience will be considered.
Pay & Benefits
At Apple, base pay is one part of our total compensation package and is determined within a range. This provides the opportunity to progress as you grow and develop within a role. The base pay range for this role is between $181,100 and $318,400, and your base pay will depend on your skills, qualifications, experience, and location.
Apple employees also have the opportunity to become an Apple shareholder through participation in Apple's discretionary employee stock programs. Apple employees are eligible for discretionary restricted stock unit awards, and can purchase Apple stock at a discount if voluntarily participating in Apple's Employee Stock Purchase Plan. You'll also receive benefits including: Comprehensive medical and dental coverage, retirement benefits, a range of discounted products and free services, and for formal education related to advancing your career at Apple, reimbursement for certain educational expenses - including tuition. Additionally, this role might be eligible for discretionary bonuses or commission payments as well as relocation. Learn more about Apple Benefits
Note: Apple benefit, compensation and employee stock programs are subject to eligibility requirements and other terms of the applicable plan or program.

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About Apple

Sourced by ZipRecruiter

Imagine what you could do here! At Apple, new ideas have a way of becoming extraordinary products, services, and customer experiences very quickly. Bring passion and dedication to your job and there's no telling what you could accomplish. Dynamic, intelligent people and inspiring, innovative technologies are the norm here. The people who work here have reinvented entire industries with all Apple Hardware products. The same real passion for innovation that goes into our products also applies to our practices strengthening our dedication to leave the world better than we found it.

Industry

Computer and electronic product manufacturing

Company size

10,000+ Employees

Headquarters location

Cupertino, CA, US

Year founded

1976