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Xgboost Jobs (NOW HIRING)

Steampunk

Data Scientist

Mclean, VA · On-site

$125K - $160K/yr

Develop machine learning models using frameworks such as scikit-learn, XGBoost, PyTorch, TensorFlow, or similar libraries. * Perform feature engineering, dataset preparation, and model optimization ...

Red Hat, Inc.

$103K - $140K/yr

... XGBoost and Scikit-learn, constructing deep learning architectures using Keras, and designing time-series forecasting models for event-based user adoption prediction. * Manage CI/CD pipelines using ...

Steampunk

Data Scientist

Mclean, VA

$125K - $160K/yr

Develop machine learning models using frameworks such as scikit-learn, XGBoost, PyTorch, TensorFlow, or similar libraries. * Perform feature engineering, dataset preparation, and model optimization ...

Xforia, Inc.

Data Scientist

... XGBoost) o Understanding of deep learning frameworks (TensorFlow/PyTorch) o Distributed computing (Spark/Scala) o Orchestration tools such as Apache Airflow o CI/CD pipelines o Agile o Git and ...

Redhat

Senior Data Engineer

Raleigh, NC · Remote

$103K - $140K/yr

... XGBoost and Scikit-learn, constructing deep learning architectures using Keras, and designing time-series forecasting models for event-based user adoption prediction. * Manage CI/CD pipelines using ...

Steampunk

Data Scientist

Mclean, VA · On-site

$125K - $160K/yr

Develop machine learning models using frameworks such as scikit-learn, XGBoost, PyTorch, TensorFlow, or similar libraries. * Perform feature engineering, dataset preparation, and model optimization ...

Tror AI for everyone

MLOPS Engineer

Concord, CA · On-site

Strong proficiency in Java and Python , SQL, and ML libraries (e.g., scikit-learn, XGBoost, TensorFlow, PyTorch). * Experience with cloud platforms and containerization (Docker, Kubernetes). * Hands ...

Pattern AI

Machine Learning Engineer

San Mateo, CA · On-site +1

Broad familiarity with the Python ecosystem and common libraries including Scikit-Learn, XGBoost, PyTorch, Keras, Tensorflow, Pandas, and common ML cloud services. * Familiarity with CNNs, RNN, LSTMs ...

Amplify Education, Inc.

Data Scientist

$100K - $132K/yr

SARIMA, prophet, xGBoost) * Expert user of Python for data analysis tasks (data cleaning, manipulation, analysis). * Expert user of SQL, especially its use in data analysis tasks. * Experience ...

JP Morgan Chase

CCB Risk Program Associate

Wilmington, DE · On-site

$57K - $57K/yr

In-depth knowledge of advanced machine learning algorithms, including logistic regression, XGBoost, Deep Neural Networks (CNN and RNN), clustering, and recommendation systems, with expertise in model ...

Pattern AI

Machine Learning Engineer

San Mateo, CA

Broad familiarity with the Python ecosystem and common libraries including Scikit-Learn, XGBoost, PyTorch, Keras, Tensorflow, Pandas, and common ML cloud services. * Familiarity with CNNs, RNN, LSTMs ...

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How much do xgboost jobs pay per hour?

As of Aug 20, 2026, the average hourly pay for xgboost in the United States is $36.26, according to ZipRecruiter salary data. Most workers in this role earn between $24.28 and $39.66 per hour, depending on experience, location, and employer.

What is XGBoost?

XGBoost stands for eXtreme Gradient Boosting and is an open-source machine learning library that provides an efficient and scalable implementation of gradient boosting algorithms. It is commonly used for supervised learning tasks, such as classification and regression, due to its high performance, speed, and ability to handle missing values. XGBoost supports parallel processing, regularization to prevent overfitting, and can be used with various programming languages like Python, R, and Julia. Its popularity stems from its success in many machine learning competitions and real-world applications.

What types of projects or datasets do professionals commonly work with when using XGBoost in a machine learning role?

Professionals using XGBoost often tackle projects involving structured data, such as customer analytics, credit scoring, fraud detection, and sales forecasting. XGBoost is particularly valued for its speed and accuracy with large tabular datasets, making it a popular choice in finance, healthcare, and e-commerce. On a daily basis, you may collaborate with data engineers to preprocess data, work with data scientists to tune hyperparameters, and communicate findings to business stakeholders. The role typically involves iterating on feature engineering, model evaluation, and integrating models into production pipelines.

What are the key skills and qualifications needed to thrive as a machine learning engineer specializing in XGBoost, and why are they important?

To thrive as a Machine Learning Engineer specializing in XGBoost, you need a strong background in statistics, data analysis, and programming (especially Python), often supported by a degree in computer science or a related field. Proficiency with XGBoost, data preprocessing libraries (like pandas and NumPy), and experience with machine learning platforms such as scikit-learn or TensorFlow are typically required. Analytical thinking, problem-solving, and effective communication are essential soft skills for interpreting results and collaborating with stakeholders. These skills ensure accurate model development, efficient implementation, and impactful business outcomes from machine learning projects.

What is the difference between Xgboost vs Data Scientist?

AspectXgboostData Scientist
Primary RoleDeveloping and tuning machine learning models, especially gradient boosting algorithmsAnalyzing data, building models, and deriving insights across various techniques
Required SkillsProgramming (Python, R), machine learning, data preprocessingStatistics, programming, data visualization, machine learning
Work EnvironmentData science teams, machine learning projects, software developmentResearch, data analysis, cross-functional collaboration

While Xgboost is a specific machine learning algorithm used within data science projects, a Data Scientist encompasses a broader role involving data analysis, modeling, and insights. Xgboost is a tool often employed by Data Scientists to improve predictive performance, but the Data Scientist's responsibilities extend beyond just implementing algorithms.

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What job categories do people searching Xgboost jobs look for?

The top searched job categories for Xgboost jobs are:

Xgboost jobs near you
Infographic showing various Xgboost job openings in the United States as of August 2026, with employment types broken down into 80% Full Time, and 20% Contract. Highlights an 100% In-person job distribution, with an average salary of $75,411 per year, or $36.3 per hour.

Staff Research Scientist, AdTech Product Innovation

Cognitiv

San Mateo, CA • On-site, Remote

$200K - $270K/yr

Full-time

Re-posted 13 hours ago

Job description

The Role

We are seeking a Staff Research Scientist who can drive innovation through deep technical expertise and hands-on execution. You'll contribute to cutting-edge research in deep learning and LLMs while advancing Cognitiv's real-time bidding and recommendation systems at production scale. This role sits at the intersection of applied research and high-performance machine learning systems.

Location: This position will be located in Bellevue, WA office with a hybrid work schedule of 3 days in office (Mon/Tue/Wed) and 2 days remote (Thursday/Friday).

What You'll Do
  • Drive Research & Innovation. Design, prototype, and evaluate advanced machine learning and deep learning approaches, with a focus on recommendation systems, real-time bidding, and LLM-driven applications.
  • Stay Hands-On. Contribute directly through coding, experimentation, model development, and technical problem-solving across the full ML lifecycle.
  • Advance AdTech Performance. Improve model accuracy, scalability, and efficiency to drive ad targeting, bidding performance, and audience relevance.
  • Build Production-Ready ML Systems. Partner closely with engineering and infrastructure teams to deploy, optimize, and monitor machine learning models in large-scale production environments.
  • Explore Emerging Technologies. Stay current with advancements in deep learning, transformers, and LLM research, identifying practical opportunities to apply new techniques within Cognitiv's platform.
  • Collaborate Cross-Functionally. Work closely with data science, engineering, product, and platform teams to solve complex technical challenges and deliver impactful ML solutions.
  • Contribute Technical Expertise. Provide thoughtful technical input through design discussions, experimentation reviews, and collaboration with other researchers and engineers.
Tech Stack
  • Core Tools - Python, PyTorch, deep learning architectures (transformers, recommendation models).
  • Traditional ML - XGBoost, PCA.
  • Big Data / Infra - Spark, Hadoop, distributed training systems.
  • Cloud Platforms - AWS, GCP, or Azure.
  • Bonus - C++.
Who You Are
  • Experienced ML Researcher/Engineer: Master's or Ph.D. in Computer Science, Statistics, Electrical Engineering, or a related field, with 5-7+ years of experience in machine learning R&D or applied ML.
  • Deep Learning & LLM Expertise: Strong technical expertise in PyTorch, transformers, and Large Language Models (LLMs), including large-scale training, fine-tuning, and optimization of deep neural networks.
  • Machine Learning Breadth: Strong understanding of both deep learning and traditional ML techniques (e.g., XGBoost, PCA), with the ability to apply the right approach to the right problem.
  • Engineering Excellence: Proficiency in Python with strong foundations in algorithms, data structures, and software engineering principles; experience building models in real-time, high-throughput systems (e.g., recommender systems, adtech).
  • Production Experience: Hands-on experience developing, deploying, and optimizing machine learning models in production environments, including distributed systems, cloud platforms (AWS, GCP, Azure), and big data frameworks (Hadoop, Spark).
  • Collaborative Communicator: Strong written and verbal communication skills with the ability to work effectively across research and engineering teams in a fast-paced environment.
Bonus Points If You Have
  • AdTech & RTB Experience. Prior exposure to advertising technology and real-time bidding (RTB) systems is a strong plus.
  • Distributed Systems & Cloud. Familiarity with big data frameworks (Spark, Hadoop) and cloud platforms (AWS, GCP, Azure).
  • C++ Skills. Strong C++ programming ability is a significant advantage alongside Python expertise.
  • Research & Community Impact. A track record of published research or meaningful contributions to the machine learning community.
  • Bridging Research and Production. Experience translating research ideas into scalable, production-grade machine learning systems.

Salary: $200,000 - $270,000 USD Base Salary + Equity

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