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Spacy Jobs (NOW HIRING)

RADGOV INC

Senior Data Scientist

Washington, DC · On-site

... Spacy, XGBoost Statistical analysis: Advanced knowledge of statistical modeling, data analysis techniques, and problem-solving skills Ability to work independently and collaboratively, taking ...

Savant Bio

AI/ML Engineer

Manhattan, NY · On-site

... Face, spaCy) • Experience working with large-scale unstructured or semi-structured data • Practical knowledge of LLMs: prompt engineering, evaluation, fine-tuning, observability • Comfort ...

Tror AI for everyone

Data Scientist

Baltimore, MD · On-site

... SpaCy, or Transformers. • Build and integrate solutions using Large Language Models (LLMs) and Generative AI frameworks. • Perform data cleaning, preprocessing, and feature engineering on ...

Kaizen Stackup

Agent Engineer

San Francisco, CA · On-site

$17.75 - $23.50/hr

Familiarity with natural language processing techniques and libraries (e.g., NLTK, spaCy) * Knowledge of reinforcement learning and decision-making algorithms * Strong problem-solving skills and ...

Headspace

Staff Machine Learning Engineer

... spaCy) • Experience with unit, integration, and end-to-end testing, version control • Strong problem solving and communication skills and ability to influence across internal organizations • ...

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How much do spacy jobs pay per year?

As of Jul 21, 2026, the average yearly pay for spacy in the United States is $141,976.00, according to ZipRecruiter salary data. Most workers in this role earn between $121,500.00 and $163,500.00 per year, depending on experience, location, and employer.

What are some common challenges faced by professionals working with spaCy in natural language processing projects?

Professionals using spaCy often encounter challenges such as customizing pre-trained models to fit domain-specific language, handling large-scale text data efficiently, and integrating spaCy pipelines with other machine learning frameworks. Additionally, staying updated with frequent library updates and best practices can be demanding. Collaboration with data scientists and software engineers is typically necessary to ensure seamless deployment and scaling of NLP solutions in production environments.

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

To thrive as an NLP Engineer specializing in spaCy, you need a solid background in computer science, linguistics, and machine learning, often supported by a relevant degree. Proficiency with Python programming, the spaCy library, and experience with tools like Jupyter Notebooks and version control systems are typically required. Strong analytical thinking, problem-solving skills, and effective communication help you design and implement robust language models and collaborate with multidisciplinary teams. These skills and qualities are crucial for developing, optimizing, and deploying high-quality NLP solutions that meet real-world business needs.

What is a Spacy job or role?

A Spacy job typically refers to a professional who works with spaCy, an open-source natural language processing (NLP) library in Python. These roles often involve developing, implementing, or maintaining NLP applications such as text classification, named entity recognition, or information extraction using spaCy. Professionals in this field may work as data scientists, NLP engineers, or machine learning specialists, leveraging spaCy's efficient and easy-to-use tools to process and analyze large volumes of text data. Familiarity with Python programming and a background in linguistics or machine learning are often beneficial for such roles.

Is spaCy still relevant?

SpaCy is a widely used open-source library for natural language processing (NLP) tasks, including tokenization, named entity recognition, and part-of-speech tagging. It remains relevant for NLP roles due to its efficiency, ease of use, and active development, making it a valuable skill for data scientists and NLP engineers. Familiarity with Python and machine learning concepts enhances job prospects involving spaCy.

Which companies use spaCy?

Many companies across industries use spaCy for natural language processing tasks, including tech firms, financial institutions, and healthcare organizations. It is popular among data scientists and developers for building NLP applications due to its efficiency and ease of use.

What is spaCy used for?

SpaCy is a popular open-source library used by NLP professionals and data scientists for natural language processing tasks such as tokenization, part-of-speech tagging, named entity recognition, and dependency parsing. It is designed for efficient processing of large text datasets and integrates well with machine learning workflows. Knowledge of Python and NLP concepts is helpful when working with spaCy in a professional setting.

What is the meaning of spaCy?

In the context of a job related to natural language processing, spaCy is an open-source software library used for advanced text analysis and processing. It provides tools for tasks such as tokenization, part-of-speech tagging, and named entity recognition, often used by data scientists and NLP engineers. Proficiency with Python and understanding of NLP concepts are typically required for roles involving spaCy.
More about Spacy jobs
What are the most commonly searched types of Spacy jobs? The most popular types of Spacy jobs are:
What job categories do people searching Spacy jobs look for? The top searched job categories for Spacy jobs are:
Spacy jobs near you
Infographic showing various Spacy job openings in the United States as of July 2026, with employment types broken down into 97% Full Time, 2% Part Time, and 1% Contract. Highlights an 78% Physical, 4% Hybrid, and 18% Remote job distribution, with an average salary of $141,976 per year, or $68.3 per hour.
Senior Data Scientist

Senior Data Scientist

RADGOV INC

Washington, DC • On-site

Other

Posted 8 days ago

Job description

The AI Lab operates as a small, agile team where practitioners are expected to move between research, development, and deployment activities. This position will contribute to strategy while doing hands-on technical work, building models, training systems, evaluating performance, and deploying solutions. The AI Lab collaborates closely with DCCA's Data Analytics and Risk and Surveillance sections, and coordinates with the Board's enterprise technology on infrastructure, governance, and compliance matters.

Responsibilities:
AI/ML and Generative AI Development
Deployment and Operations:
Collaboration, Communication and Agile Practices
Governance and Compliance Awareness

Required Qualifications:
At least six years of hands-on experience developing, deploying, and maintaining AI/ML applications within a large, professional, or academic organization
Bachelor's degree in Computer Science, Data Science, Statistics, Machine Learning, or related technology field (Master's degree preferred)
Expert proficiency in Python or R for data science development; experience with additional programming languages
Production deployment experience: Demonstrated ability to build, deploy, and maintain AI/ML applications in cloud environments, including containerization and basic CI/CD practices
Application development: Proficiency building interactive applications and dashboards using frameworks such as Streamlit, Dash, Flask, RShiny, or similar
Data visualization: Strong experience creating visualizations and dashboards using Python/R libraries, Tableau, Power BI, or similar tools to communicate technical concepts to non-technical audiences
AI/ML expertise: Advanced knowledge of machine learning, NLP (text normalization, Named Entity Recognition, POS tagging, word embeddings), and Generative AI technologies; experience with frameworks such as Scikit-learn, Spacy, XGBoost
Statistical analysis: Advanced knowledge of statistical modeling, data analysis techniques, and problem-solving skills
Ability to work independently and collaboratively, taking ownership of solutions from conception through production deployment

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