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Xgboost Jobs (NOW HIRING)

Staffingine LLC

AI Engineer

Phoenix, AZ · On-site

... XGBoost and PyTorch for model development and training Design and optimize data processing workflows using SQL and other pipeline tools Integrate and manage APIs to support AI applications and ...

1 point system

Senior Data Scientist

New York, NY · Remote

Expertise in Python, SQL, and Spark, and a broad array of machine learning frameworks (Scikit-Learn, XGBoost, Tensorflow, PyTorch, MXNet, LLM, etc). * Experience in developing and deploying solutions ...

Xforia, Inc.

Data Scientist

Pandas, Numpy, Matplotlib, Scikit-learn, LightGBM, XGBoost, OpenAI • Coursework: Data Mining, Data Visualization, Foundations of Analytics, Database Management, Web Analytics, Software Engineering ...

Epitec

Data Scientist

Southfield, MI · On-site

$128K/yr

Python, SQL, Scikit-learn, XGBoost, Prophet, ARIMA, RESTful APIs, GCP, Terraform, Docker, VBA, and ODBC. Will also accept any suitable combination of education, training, and/or experience. Employer ...

Tata Consultancy Services

AI ML Data Scientist

Cary, NC · On-site

... XGBoost-in various business problems (AML, fraud detection, mortgage default, foreclosure, credit risk management, price prediction and optimization) • Strong leadership and capacity to work as a ...

cloudingest inc

Gen AI

Dallas, TX · On-site

XGBoost * PyTorch * TensorFlow (preferred) ### Data & Analytics Platforms * Snowflake * Databricks * SQL * Vector Databases * Feature Stores * Data Warehousing Concepts ### Backend & APIs * Python

New

Worthland

Lead Risk Manager, Payment Fraud

Boston, MA · Hybrid

$150K - $180K/yr

Sklearn, XGBoost, LightGBM. • Mandarin Chinese fluency (the company operates a bilingual EN/CN working environment; this is a hard requirement). • Based in or willing to relocate to the Greater ...

Staffingine LLC

Artificial Intelligence Engineer

Bellevue, WA · On-site

$129K - $155K/yr

Experience with ML frameworks eg scikitlearn XGBoost TensorFlow PyTorch. * Strong knowledge of statistics experimental design and causal inference. * Handson experience with data visualization tools ...

Marencor

AI/ML Data Scientist

New York, NY · On-site

Train supervised and unsupervised models using Python ( XGBoost, LightGBM, sklearn, PyTorch ) * Conduct data profiling, feature engineering, model evaluation using stratified validation * Implement ...

Worthland

Lead Risk Manager, Payment Fraud

New York, NY · Hybrid

$150K - $180K/yr

Sklearn, XGBoost, LightGBM. • Mandarin Chinese fluency (the company operates a bilingual EN/CN working environment; this is a hard requirement). • Based in or willing to relocate to the Greater ...

Euclid Innovations

ML Engineer

Charlotte, NC · On-site

Strong Python + ML frameworks (PyTorch, TensorFlow, XGBoost) * Experience with model migration / refactoring * Hands-on with: * Retraining & inference workflows * Model validation (accuracy, drift)

Roberts Recruiting

Senior Data Scientist

Boston, MA · On-site

Familiarity with Python NLP frameworks and libraries such as scikit, NumPy, and XGBoost * Experience working with engineers on algorithm development, architecture planning, and development

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How much do xgboost jobs pay per hour?

As of Jul 9, 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.

Is XGBoost still popular?

XGBoost remains a popular machine learning algorithm used in data science and AI roles due to its high performance and efficiency in structured data tasks. It is widely valued for its speed, scalability, and effectiveness in competitions like Kaggle, making it a common skill for data analysts and machine learning engineers.

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 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.

Is 40 too late for data science?

Age is generally not a barrier to entering data science roles, including positions involving XGBoost and other machine learning tools. Many professionals successfully transition into data science later in their careers by acquiring relevant skills, certifications, and experience. Employers value skills and problem-solving ability over age, making it possible to start or switch into data science at 40 or older.

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.

Is data science still worth it in 2026?

Data science remains a valuable field in 2026, with roles involving machine learning models like XGBoost, data analysis, and predictive modeling. Skills in programming, statistics, and tools such as Python and SQL are essential for success in this evolving industry.

Is ML a high paying job?

Machine learning roles, including positions involving XGBoost, are generally well-paid due to high demand for data science and AI skills. Salaries vary based on experience, location, and industry, but professionals with expertise in machine learning tools and algorithms often earn above average wages in the tech sector.
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Xgboost jobs near you
Infographic showing various Xgboost job openings in the United States as of July 2026, with employment types broken down into 96% Full Time, and 4% Contract. Highlights an 89% Physical, 1% Hybrid, and 10% Remote job distribution, with an average salary of $75,411 per year, or $36.3 per hour.
AI Engineer

AI Engineer

Staffingine LLC

Phoenix, AZ • On-site

Contractor

Re-posted 13 days ago

Job description

Job Title: AI Engineer
Job Location: Phoenix, AZ
Job Type: Contract

Job Description:

  1. Develop and implement AI solutions using Python and AI frameworks such as Langgraph and Langchain Work with vector databases like Pinecone to manage and query highdimensional data Build and maintain machine learning pipelines for data processing and model deployment Utilize ML frameworks including XGBoost and PyTorch for model development and training Design and optimize data processing workflows using SQL and other pipeline tools Integrate and manage APIs to support AI applications and services Collaborate with crossfunctional teams to deliver scalable AIdriven products 

Roles and Responsibilities 

  1. Design develop and deploy AI and machine learning models using Python and relevant AI stacks Manage vector databases to enhance data retrieval and storage efficiency Build robust ML pipelines to automate data ingestion preprocessing and model training Apply advanced ML frameworks such as XGBoost and PyTorch for predictive analytics Develop data processing solutions involving SQL and pipeline orchestration Create and maintain APIs for seamless integration of AI components Collaborate with data scientists engineers and stakeholders to ensure project success Continuously monitor and improve AI system performance and scalability

Skills

Mandatory Skills : Agentic Framework