3 NLP Engineer Resume Examples & Writing Guide

3 NLP engineer resume samples and a step-by-step writing guide. Learn what it takes to create an NLP engineer resume that grabs attention and lands interviews. Discover how to highlight your natural language processing skills and experience to impress hiring managers. Includes detailed breakdowns of winning resumes to help you write your own.

Writing a resume can be hard, especially for NLP engineers. There's a lot to consider - what to include, how to showcase your skills, and how to make your resume shine. But don't worry, this guide is here to help.

In this article, you'll find three NLP engineer resume examples that show you exactly what a great resume looks like. You'll also get step-by-step advice on how to write each section of your resume, from the summary to the skills section.

The goal is to make your resume stand out to hiring managers and land you interviews at top companies. By following the tips in this guide, you'll be able to create a resume that highlights your strengths, shows off your NLP engineering experience, and impresses employers.

So if you're ready to take your NLP engineering career to the next level, keep reading. With these examples and writing tips, you'll have everything you need to build a resume that gets results.

Common Responsibilities Listed on NLP Engineer Resumes

  • Developing and implementing natural language processing models and algorithms
  • Preprocessing and cleaning text data for NLP tasks
  • Building and training language models for tasks like text classification, sentiment analysis, and named entity recognition
  • Evaluating and optimizing NLP models for performance and accuracy
  • Integrating NLP models into applications and systems
  • Collaborating with data scientists, software engineers, and subject matter experts
  • Researching and staying up-to-date with the latest NLP techniques and advancements
  • Designing and implementing deep learning architectures for NLP tasks
  • Developing and maintaining NLP pipelines and workflows
  • Analyzing and interpreting NLP model outputs and results
  • Creating and maintaining documentation for NLP models and processes
  • Participating in code reviews and contributing to best coding practices
  • Troubleshooting and debugging NLP models and systems
  • Presenting NLP solutions and findings to stakeholders and teams

How to write a Resume Summary

When you're composing the summary or objective section for your resume as an NLP Engineer, it's fundamental to remember that this is your initial opportunity to create an impression on a potential employer. Your summary should be nothing less than a crystal-clear, cogent presentation of your value proposition that succinctly exhibits your top relevant skills, experiences, and goals.

Start with a Clear Statement

Initiate with a powerful statement that encapsulates who you are professionally. Avoid generic phrases or jargon. Instead, be precise in explaining your role. In your case, specify your NLP engineer role and the related IT field.

Highlight Key Skills and Experiences

In amalgamation with your role statement, highlight the most fitting skills and experiences connected to the particular job you're seeking. Because context matters, cherry-pick only pertinent information. It might be knowledge of specific programming languages, mastery in certain AI models, proven ability with data set management, or experience in the development of machine learning algorithms, to name a few.

Set Indication of Your Professional Goals

Your professional career objectives should be represented in the summary. This doesn't mean giving an exhaustive list but rather a brief highlight of your career trajectory. Also, remember to relate this back to the position you are applying for to show alignment.

Keep it Brief and Focused

Keeping your summary concise and focused helps maintain the reader's attention. It should neither be too detailed nor too shallow, but an optimal blend of both, usually within 3-5 informative sentences.

Include Demonstrable Value

If possible, provide any demonstrable value or achievements that could solidify your parameter as a versed and valuable NLP engineer. It could be in the form of successful projects, impacts on the teams and companies, or countributions in the NLP field.

Remember, the aim of a summary is to garner interest, generate curiosity, and demonstrate your relevance to the position. By effectively linking your skills, experiences, achievements, and career objectives, you position yourself as a promising candidate at very first glance.

In conclusion, firm positioning of who you are, what you can do and how you can contribute to an organization will lay a robust foundation for the rest of your resume. Trusting in the worth of your skills and conveying that credibly will bring you one step closer to cementing that desired role.

Strong Summaries

  • Driven NLP Engineer with 5+ years of experience and a proven track record in developing innovative AI solutions for business optimization. Expert in Python, TensorFlow, and NLTK, with strong ability to apply advanced machine learning techniques to design effective Natural Language Processing systems.
  • Results-oriented NLP engineer with over 3 years of experience in machine learning, text classification, and information retrieval. Skilled in computer linguistics and statistical methods. Seeking to leverage my abilities in AI and ML to drive the success of your team.
  • Dedicated and innovative NLP Engineer with a Masters in Computer Science and specialization in Artificial Intelligence. Proficient in Python, Machine Learning and Deep Learning with a strong focus on designing intelligent NLP Systems for improved business outcomes.
  • Highly motivated NLP Engineer with expertise in Deep Learning and Text Analytics. Solid experience with open source NLP toolkits like Spacy and Gensim. Passionate about leveraging AI techniques to solve complex industry challenges.

Why these are strong?

These examples are good because they briefly discuss the candidate's years of experience, relevant skills, and career achievements. They use specific, measurable details (e.g., '5+ years of experience', 'Masters in Computer Science') to provide tangible evidence of their suitability for the job. They also mention some technical skills and tools (Python, TensorFlow, NLTK, Spacy, Gensim) which are vital for an NLP Engineer position. Furthermore, they express the candidates' motivation and goals, thus demonstrating their eagerness and commitment to contributing to the prospective employer.

Weak Summaries

  • I am an experienced NLP Engineer, and I have experience...
  • Seeking for an NLP Engineer position...
  • I want to be an NLP Engineer to enhance my skills...
  • I was good in school so hire me...
  • I am a perfect fit because I am so awesome...
  • Being an NLP engineer is my dream...
  • NLP Engineer is what I live for...
  • Let's work together so I can show you my amazing skills...
  • I work hard, play harder...

Why these are weak?

These samples are not good practices for a professional summary section because they offer no real informative content about the applicant’s qualifications, skills and experiences. They come off as overly subjective or self-indulgent. For instance: 'I am so awesome' is a subjective statement that tells the employer nothing about the applicant's skills or experiences. Also, repetitive use of the phrase 'I am', comes off as self-centered and doesn’t entice the employer to read more. Phrases like 'Seeking for an NLP Engineer position...' or 'I want to be...' are also bad choices because they focus on the applicant's needs rather than what they can offer to a potential employer. The employer already knows that the applicant wants the job, which is why they applied. What the employer needs to know is what makes the applicant a worthy candidate for the position.

Showcase your Work Experience

Writing an effective Work Experience section on your resume doesn't necessarily mean you need to showcase a long list of previous employment, especially if you're a Natural Language Processing (NLP) Engineer. Instead, this section should highlight your valuable skills, achievements, and experiences that are specifically relevant to the roles you're applying for.

Focus on Relevance

Don't include every minor job or internship you've ever had. As an NLP Engineer, your resume should focus primarily on experiences tied to NLP, machine learning and data science projects you've been part of, or any roles where you've been able to apply these skill sets. Ensure each work experience listed supports the claim that you're a capable and experienced NLP engineer.

Quantify Your Achievements

Don't underestimate the impact of numbers. When possible, use them to illustrate your starts, finishes, and everything in between. For instance, did the project you work on increase efficiency by a significant percentage? Or maybe your code sped up a data processing task? Be sure to mention that. It's a tangible way to demonstrate the value you could bring to a potential employer.

Expert Tip

Quantify your achievements and impact using concrete numbers, metrics, and percentages to demonstrate the value you brought to your previous roles.

Avoid Jargon

While it's important to mention technical skills and terminology, remember that the first person to view your resume might be from HR and may not be familiar with all the jargon. Try to explain your roles and achievements in a way that someone without an NLP background could understand them.

Use Action Verbs

Begin each bullet point or sentence with a strong action verb rather than generic phrases like 'responsible for.' This makes your resume more dynamic and showcases your proactive attitude. Words like 'Implemented', 'Developed', 'Spearheaded' can make a worthwhile difference.

Tailor for Each Application

Every role you apply for may require subtly different skills or experiences. So, tailor your work experience section for each application. Highlight your most relevant experiences and achievements that closely match the job description provided.

Remember, the aim of this section is to validate your ability as an NLP Engineer by highlighting what you have accomplished in the past. It should prove to potential employers that you have the ability to deliver and succeed in the future. Small tweaks and adjustments can create a significant difference to your application's outcome, securing that all-important interview.

Strong Experiences

  • Developed a sentiment analysis model improving customer reviews classification accuracy by 15%
  • Designed an automated text generation pipeline using Recurrent Neural Networks
  • Implemented topic modeling on customer communications leading to improved goal tracking
  • Increased data processing speed by 20% through implementing NLP transformations and deploying them on cloud infrastructure
  • Optimized the entity recognition AI system, reducing computational resources usage by 25%

Why these are strong?

A good bullet point on a resume is one that is clear, concise, and quantifies achievements whenever possible. They are typically action-oriented, starting with a powerful action verb. For example, 'developed', 'designed', 'implemented' and 'optimized' illustrate activity and responsibility. Furthermore, these examples show the impact of the candidate's contributions in their past role, be it improving classification accuracy, enhancing data processing speed, or reducing computational resource usage. Using numbers to quantify achievements helps prospective employers understand the scale of your accomplishments and can make your resume more compelling.

Weak Experiences

  • Used NLP stuff for things.
  • Did some machine learning stuff.
  • Worked a bit with computers and data.
  • Did work.
  • Had lunch with the team.
  • Turned on my computer daily.
  • Helped in some projects.
  • Also did some other stuff.

Why these are weak?

The above points are bad examples primarily due to their lack of specificity and detail. When reading through a resume, hiring managers are specifically looking for what you contributed to your previous roles and how you were useful to the company. General and vague pointers like 'used NLP stuff for things', 'did some machine learning stuff', don't really say much about your actual role and responsibilities. For example, 'had lunch with the team' is irrelevant unless it directly relates to the job you're applying for. 'Turned on my computer daily' is a task almost everyone does, it doesn't make potential employers understand your knowledge or skills. It is also essential to clearly state the projects you worked on and how it benefited the company, 'helped in some projects', or 'also did some other stuff' don't shed light on your involvement or the impact of the work performed. Therefore, these are considered as bad practices in resume writing.

Skills, Keywords & ATS Tips

The heart of any resume, for a Natural Language Processing (NLP) Engineer, or any other field, is the section about your skills. This small, yet mighty, section can make or break your chances at landing an interview.

Hard Skills

As an NLP Engineer, hard skills are the technical capacities you've learned, like programming languages, machine learning, deep learning, and of course, natural language processing. These concrete skills are often necessary to perform specific tasks or functions in your job. If you have certification in some of these hard areas, include that too. Recruiters often look for a clear listing of these technical capabilities in the skills section, as they offer a quick way to ascertain whether you're fit for the role or not.

Soft Skills

Though technical abilities are vital for an NLP Engineer, so are soft skills. Soft attributes include things like good communication, problem-solving, creativity, and adaptability. These are not task-specific, but they detail how you handle work and relationships. Sometimes, these skills can tip the scale in your favor. Even if you're applying for a highly technical role, indicating that you possess these soft qualities can make a potential employer see you as a balanced candidate.

Keywords & Applicant Tracking Systems (ATS)

Keywords and Applicant Tracking Systems (ATS) are two essential aspects often overlooked but play a significant role in whether your application gets noticed. ATS are software tools used by many companies today to scan resumes for specific keywords or phrases linked to the job description. If the software can't find these, your applications land in the discard pile.

Understanding this, it's paramount to match your skills with the keywords or phrases in the job listing. This doesn't mean you should just copy-paste from the job description. Rather, identify the important skills the job requires, and if you possess those, mention them in your skills section using the same or similar language.

Linking Skills And Keywords

The importance of the connection between your skills, keywords and ATS cannot be understated. It's about ensuring that the ATS recognizes you as a suitable candidate and thereby ensures that your CV goes under human eyes. What does this mean for you? It means that when you list your hard and soft skills, you should consider the exact keywords an ATS might be looking for. When those keywords align well with your skills, it increases your chances of being shortlisted.

Remember, a recruiter wants to quickly see your capabilities and know that you understand their needs. Build your skills section with this in mind. Your resume is a marketing tool where you're the product. Provide clear value and potential on paper by strategically merging your hard and soft skills with the right keywords.

Top Hard & Soft Skills for Full Stack Developers

Hard Skills

  • Natural Language Processing
  • Machine Learning
  • Deep Learning
  • Python Programming
  • Data Mining
  • Text Mining
  • Information Retrieval
  • Statistical Analysis
  • Algorithm Development
  • Neural Networks
  • Linguistics
  • Data Visualization
  • Information Extraction
  • Sentiment Analysis
  • Named Entity Recognition
  • Soft Skills

  • Analytical Thinking
  • Problem-Solving
  • Creativity
  • Communication
  • Teamwork
  • Adaptability
  • Critical Thinking
  • Attention to Detail
  • Time Management
  • Curiosity
  • Collaboration
  • Empathy
  • Presentation Skills
  • Leadership
  • Innovation
  • Top Action Verbs

    Use action verbs to highlight achievements and responsibilities on your resume.

  • Analyzed
  • Developed
  • Implemented
  • Designed
  • Evaluated
  • Optimized
  • Built
  • Experimented
  • Collaborated
  • Researched
  • Programmed
  • Innovated
  • Tested
  • Deployed
  • Refined
  • Modeled
  • Documented
  • Validated
  • Solved
  • Interpreted
  • Visualized
  • Communicated
  • Presented
  • Managed
  • Coordinated
  • Facilitated
  • Led
  • Inspired
  • Negotiated
  • Adapted
  • Prioritized
  • Synthesized
  • Critiqued
  • Championed
  • Empathized
  • Education

    To add your education and certificates to your resume as an NLP Engineer, start with acknowledging the importance of specificity and precise placement. Typically, you should place this information after your career summary or objective, but before your work experience. This placement ensures maximum visibility. List your college degree first, followed by any related certificates, each in a reverse chronological order (from newest to oldest). Make sure to include details of the institution, graduation year and any relevant coursework or projects. Highlight any specific skills gained during this study time that may not be evident from just the qualification and make sure these align with the job requirements. Remember, Even if the hiring manager only scans this section, they will spot these crucial pieces of information.

    Resume FAQs for NLP Engineers


    What is the ideal resume format for an NLP Engineer?


    The most recommended resume format for an NLP Engineer is the reverse-chronological format. This format highlights your work experience and technical skills, which are crucial for NLP roles.


    How long should an NLP Engineer resume be?


    An NLP Engineer resume should typically be one page long for candidates with less than 10 years of experience, and up to two pages for those with more extensive experience.


    What are the most important sections to include in an NLP Engineer resume?


    The most important sections to include in an NLP Engineer resume are: a summary or objective statement, technical skills, work experience, projects, and education.


    How can I highlight my NLP skills on my resume?


    To highlight your NLP skills, create a dedicated 'Technical Skills' section and list relevant skills such as natural language processing, machine learning, deep learning, Python, TensorFlow, NLTK, and any specific NLP libraries or frameworks you're proficient in.


    Should I include personal projects on my NLP Engineer resume?


    Yes, including personal projects on your NLP Engineer resume is highly recommended. It demonstrates your passion for the field, hands-on experience, and ability to apply NLP concepts in real-world scenarios.


    How can I make my NLP Engineer resume stand out?


    To make your NLP Engineer resume stand out, quantify your achievements with metrics, highlight any publications or conference presentations, showcase your problem-solving skills through project descriptions, and tailor your resume to the specific job you're applying for.

    NLP Engineer Resume Example

    An NLP (Natural Language Processing) Engineer is responsible for developing sophisticated algorithms and models to extract insights from human language data. Their core tasks involve designing and implementing NLP pipelines, training machine learning models on text datasets, and optimizing performance. When writing a resume for an NLP Engineer role, emphasize your experience with key NLP techniques like text classification, named entity recognition, and sentiment analysis. Highlight projects that showcase your proficiency in Python, deep learning frameworks (TensorFlow, PyTorch), and NLP libraries (NLTK, spaCy). Demonstrate your problem-solving abilities, attention to detail, and a strong grasp of linguistic concepts. Quantify your accomplishments with metrics that illustrate the impact of your work.

    Martha Shelton
    (993) 532-5889
    NLP Engineer

    Highly skilled NLP Engineer with a passion for developing cutting-edge natural language processing solutions. Proven track record of delivering innovative projects that enhance user experience and drive business growth. Adept at collaborating with cross-functional teams to implement state-of-the-art NLP techniques and algorithms.

    Work Experience
    Senior NLP Engineer
    06/2021 - Present
    • Led a team of 5 engineers to develop and deploy advanced NLP models for Alexa, improving user engagement by 30%.
    • Implemented a novel deep learning approach for sentiment analysis, achieving an accuracy of 95% on customer reviews.
    • Collaborated with product managers and UX designers to create a personalized content recommendation system, increasing user retention by 20%.
    • Optimized NLP pipelines for scalability and performance, reducing latency by 40% and enabling real-time processing of large datasets.
    • Conducted regular workshops and training sessions to mentor junior engineers and promote best practices in NLP development.
    NLP Research Scientist
    01/2019 - 05/2021
    • Conducted cutting-edge research in natural language understanding, focusing on enhancing machine comprehension and question answering capabilities.
    • Developed a novel transfer learning approach for low-resource languages, enabling accurate NLP models with limited training data.
    • Published 3 research papers in top-tier NLP conferences, receiving best paper award at ACL 2020.
    • Collaborated with IBM Watson team to integrate research findings into commercial NLP products, improving accuracy and performance.
    • Presented research findings at international conferences and hosted workshops to share knowledge with the NLP community.
    NLP Software Engineer
    08/2017 - 12/2018
    • Developed and maintained NLP components for Google Assistant, improving user query understanding and response generation.
    • Implemented advanced named entity recognition and disambiguation techniques, increasing accuracy by 15%.
    • Optimized NLP models for low-latency inference, enabling real-time processing of user queries on mobile devices.
    • Collaborated with cross-functional teams to integrate NLP capabilities into various Google products, enhancing user experience.
    • Participated in hackathons and innovation challenges, contributing to the development of novel NLP applications.
  • Natural Language Processing (NLP)
  • Deep Learning
  • Machine Learning
  • Python
  • PyTorch
  • TensorFlow
  • Hugging Face Transformers
  • spaCy
  • NLTK
  • NLP Pipelines
  • Named Entity Recognition (NER)
  • Sentiment Analysis
  • Text Classification
  • Information Retrieval
  • Language Modeling
  • Transfer Learning
  • NLP Research
  • Education
    Ph.D. in Computer Science, specializing in Natural Language Processing
    09/2013 - 06/2017
    Stanford University, Stanford, CA
    B.S. in Computer Science
    09/2009 - 05/2013
    University of California, Berkeley, Berkeley, CA
    Senior NLP Engineer Resume Example

    Senior NLP Engineers develop and enhance AI systems that process human language. Required: deep expertise in machine learning, NLP algorithms/tools, programming, and communication skills. Resume tips: Highlight specialized NLP skills (e.g. language models, natural language understanding/generation), spell out technical accomplishments through portfolio projects, quantify impact through metrics. Clearly convey ability to lead complex NLP initiatives.

    Tamara Scott
    (431) 480-3843
    Senior NLP Engineer

    Highly skilled and experienced Senior NLP Engineer with a proven track record of developing and deploying state-of-the-art natural language processing solutions. Adept at leveraging cutting-edge techniques to extract insights from complex unstructured data, enabling data-driven decision making. Passionate about pushing the boundaries of NLP and delivering impactful results.

    Work Experience
    Senior NLP Engineer
    01/2020 - Present
    • Led the development of a large-scale sentiment analysis system, improving accuracy by 25% and reducing processing time by 40%.
    • Implemented advanced named entity recognition models using deep learning, achieving an F1 score of 0.95 on benchmark datasets.
    • Developed a natural language question answering system that outperformed existing solutions by 20% in terms of accuracy and response time.
    • Collaborated with cross-functional teams to integrate NLP capabilities into various Google products, enhancing user experience and engagement.
    • Mentored junior engineers and conducted technical workshops to promote best practices in NLP development.
    NLP Research Engineer
    06/2017 - 12/2019
    Microsoft Research
    • Conducted research on advanced language models and transfer learning techniques, resulting in 3 peer-reviewed publications in top-tier NLP conferences.
    • Developed a novel approach for multi-lingual text classification, achieving state-of-the-art performance on benchmark datasets for 10+ languages.
    • Implemented a scalable text summarization system using transformers, reducing summary generation time by 50% while maintaining high quality.
    • Collaborated with academic partners to organize a workshop on 'Advances in NLP for Low-Resource Languages', fostering knowledge sharing and community building.
    • Contributed to open-source NLP libraries and tools, with commits merged into popular repositories like spaCy and HuggingFace Transformers.
    Machine Learning Engineer
    09/2015 - 05/2017
    • Developed machine learning models for product recommendation and personalization, improving click-through rates by 15%.
    • Implemented a real-time anomaly detection system for identifying fraudulent reviews, reducing manual review efforts by 30%.
    • Optimized model training pipelines using distributed computing frameworks, reducing training time from days to hours.
    • Collaborated with product teams to define and track key performance metrics, ensuring the effectiveness of deployed ML solutions.
    • Conducted code reviews and provided technical guidance to junior engineers, promoting code quality and best practices.
  • Natural Language Processing (NLP)
  • Deep Learning
  • Machine Learning
  • Python
  • PyTorch
  • TensorFlow
  • spaCy
  • HuggingFace Transformers
  • BERT
  • GPT
  • Text Classification
  • Named Entity Recognition
  • Sentiment Analysis
  • Information Retrieval
  • Data Science
  • Education
    Ph.D. in Computer Science
    09/2012 - 08/2015
    Stanford University,
    M.S. in Computer Science
    09/2010 - 06/2012
    Stanford University,
    B.S. in Computer Science
    09/2006 - 05/2010
    University of California, Berkeley,
    Natural Language Processing Engineer Resume Example

    As a Natural Language Processing (NLP) Engineer, you design and implement systems that enable computers to understand and generate human language. This multidisciplinary role requires expertise in programming, machine learning algorithms, and linguistic fundamentals. When crafting your resume, vividly describe projects where you built robust NLP models, emphasizing technical proficiencies like Python, TensorFlow, and domain knowledge. Quantify successes with metrics that showcase your ability to enhance language processing accuracy and efficiency. Tailor each application to the specific NLP role's requirements for maximum impact.

    Susan Boyd
    (591) 501-4255
    Natural Language Processing Engineer

    Innovative Natural Language Processing Engineer with a proven track record of developing cutting-edge NLP solutions for diverse industries. Skilled in machine learning, deep learning, and computational linguistics, with a passion for leveraging state-of-the-art techniques to solve complex language-related challenges. Recognized for delivering high-quality, scalable, and efficient NLP systems that drive business value and enhance user experiences.

    Work Experience
    Senior NLP Engineer
    06/2021 - Present
    • Led a team of 5 engineers to develop and deploy a large-scale sentiment analysis system for Amazon product reviews, improving customer satisfaction by 25%.
    • Implemented a novel named entity recognition model using transformer-based architectures, resulting in a 15% increase in accuracy compared to the existing solution.
    • Collaborated with cross-functional teams to integrate NLP capabilities into Alexa, enhancing user experience and engagement.
    • Optimized machine learning pipelines for training and inference, reducing latency by 30% and enabling real-time processing of user queries.
    • Conducted research on advanced NLP techniques, resulting in 2 patent applications and 3 publications in top-tier conferences.
    NLP Research Scientist
    09/2018 - 05/2021
    IBM Watson
    • Developed a state-of-the-art question-answering system leveraging deep learning and knowledge graphs, improving accuracy by 20% over the existing system.
    • Led research efforts on multi-lingual NLP, enabling the expansion of Watson's capabilities to support 10+ languages.
    • Collaborated with product teams to design and implement NLP features for IBM Watson Discovery, enhancing the platform's information retrieval capabilities.
    • Published 5 research papers in top NLP conferences and journals, contributing to the advancement of the field.
    • Mentored junior researchers and interns, fostering a culture of innovation and continuous learning within the team.
    NLP Software Engineer
    07/2016 - 08/2018
    • Contributed to the development of GPT-2, a groundbreaking language model capable of generating human-like text.
    • Implemented distributed training techniques to accelerate model training, reducing training time by 40%.
    • Developed a text summarization module for OpenAI's API, enabling users to generate concise summaries of long articles.
    • Collaborated with research scientists to design and conduct experiments on transfer learning and few-shot learning techniques.
    • Participated in open-source projects and contributed to the NLP community through code reviews and technical blog posts.
  • Natural Language Processing (NLP)
  • Machine Learning
  • Deep Learning
  • Python
  • PyTorch
  • TensorFlow
  • Hugging Face Transformers
  • spaCy
  • NLTK
  • Scikit-learn
  • Named Entity Recognition (NER)
  • Sentiment Analysis
  • Question Answering
  • Text Summarization
  • Information Retrieval
  • Language Modeling
  • Transformer Architectures
  • Multilingual NLP
  • Transfer Learning
  • Few-Shot Learning
  • Education
    Ph.D. in Computer Science
    09/2012 - 06/2016
    Stanford University, Stanford, CA
    B.S. in Computer Science
    09/2008 - 05/2012
    Massachusetts Institute of Technology (MIT), Cambridge, MA