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Job Title: Machine Learning Engineer

Location: Austin, TX / Remote within the U.S.

Job Type: Contract / Full-Time

About the Position

Join an AI and Data Science team developing production-grade predictive machine learning solutions. We are looking for an engineer who can work across the full ML lifecycle and turn business problems into reliable production models.

ResponsibilitiesDesign and develop predictive ML models.Build data and feature engineering pipelines using Python and PySpark.Develop scalable ML workflows.Train, evaluate, tune, and deploy machine learning models.Build MLOps pipelines for automated training and retraining.Monitor production models and analyze model performance.Work with AWS cloud infrastructure.Collaborate with Data Science and Software Engineering teams.Integrate models into enterprise applications.Support predictive forecasting and AI initiatives.Projects May IncludeConstruction-duration prediction.Permit-processing prediction.Construction-cost prediction.Production model optimization.Automated design and decision-support applications.Required

Python, PySpark, Scikit-learn, AWS, MLOps, production ML, feature engineering, model deployment, monitoring, and model lifecycle management.

Preferred

XGBoost/LightGBM, MLflow, PyTorch/TensorFlow, NLP/LLMs, CI/CD, workflow orchestration, forecasting, and distributed ML.

GCS is acting as an Employment Business in relation to this vacancy.

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Job Title: Machine Learning Engineer

Location: Dallas-Fort Worth, TX / Remote within the U.S.

Job Type: Contract / Full-Time

Position Summary

We are seeking an experienced Machine Learning Engineer to build and maintain production predictive AI solutions.

The ideal candidate has strong Python and PySpark experience and understands the complete ML lifecycle, including model development, deployment, monitoring, and retraining.

ResponsibilitiesDevelop predictive machine learning models.Build scalable PySpark pipelines.Perform feature engineering and statistical analysis.Train and evaluate ML models.Deploy models into production environments.Develop automated MLOps and retraining pipelines.Monitor production model performance.Troubleshoot model performance and data issues.Use AWS for scalable ML workloads.Collaborate with Data Scientists, Engineers, and business stakeholders.Required Skills5+ years ML Engineering/AI Engineering experience.Python.PySpark/Spark.Scikit-learn.AWS.MLOps.Model deployment and monitoring.Feature engineering.Predictive modeling.Preferred

XGBoost, LightGBM, MLflow, PyTorch/TensorFlow, NLP/LLM, CI/CD, orchestration, forecasting, and distributed ML.

GCS is acting as an Employment Business in relation to this vacancy.

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Job Title: Machine Learning Engineer

Location: Boston, MA / Remote within the U.S.

Job Type: Contract / Full-Time

Position Overview

We are hiring a Machine Learning Engineer to develop scalable predictive models and production ML pipelines.

This role is suited for an engineer who enjoys solving complex problems with data and has experience taking ML models from experimentation into production.

ResponsibilitiesDevelop predictive machine learning solutions.Build scalable data-processing pipelines using Python and PySpark.Perform feature engineering and data analysis.Train, evaluate, and optimize ML models.Deploy models into production.Build MLOps pipelines for training, deployment, monitoring, and retraining.Monitor model performance and continuously improve production models.Develop AWS-based ML solutions.Work with large structured and unstructured datasets.Collaborate with engineering, data science, and business teams.Contribute to NLP/LLM/GenAI initiatives as needed.Required

5+ years ML/AI Engineering experience, strong Python, PySpark, Scikit-learn, AWS, MLOps, production ML deployment, feature engineering, and model evaluation.

Preferred

XGBoost, LightGBM, MLflow, PyTorch, TensorFlow, NLP/LLM, forecasting, CI/CD, workflow orchestration, and distributed ML.

GCS is acting as an Employment Business in relation to this vacancy.

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Job Title: Machine Learning Engineer

Location: Chicago, IL / Remote within the U.S.

About the Role

We are looking for an experienced Machine Learning Engineer to develop, deploy, and maintain production predictive AI solutions.

You will work on real-world machine learning problems involving forecasting, prediction, optimization, and automation.

What You Will DoDevelop predictive ML models using Python and Scikit-learn.Build scalable PySpark data-processing pipelines.Perform feature engineering and statistical analysis.Train, evaluate, and optimize machine learning models.Deploy ML models into production.Build automated MLOps pipelines.Implement model monitoring and retraining processes.Work with AWS-based ML infrastructure.Investigate production model performance and improve existing models.Collaborate with Data Scientists, Software Engineers, and business stakeholders.Support NLP, LLM, and Generative AI initiatives.Example Use CasesPredict construction completion dates.Predict government permit processing times.Forecast construction costs.Improve existing production ML models.Develop ML-driven automation and decision-support solutions.Required Skills5+ years ML Engineering/AI Engineering experience.Python.PySpark/Spark.Scikit-learn.AWS.MLOps.ML deployment.Feature engineering.Model evaluation and monitoring.Production ML experience.Preferred

XGBoost/LightGBM, MLflow, PyTorch/TensorFlow, NLP/LLMs, CI/CD, workflow orchestration, forecasting, and distributed ML.

GCS is acting as an Employment Business in relation to this vacancy.

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Machine Learning Engineer - AI / Predictive Analytics

We're looking for a Machine Learning Engineer to join a growing AI/ML team working on predictive solutions for Comcast's construction organization.

The team is solving several interesting problems with machine learning-from predicting construction timelines and permitting delays to forecasting project costs and exploring automated construction design.

This is a hands-on role for someone who enjoys data, feature engineering, predictive modeling, experimentation, and production ML.

What You'll Be Working On

Predicting Construction Timelines
Improve an existing model that predicts how long construction jobs will take.

Predicting Permit Timelines
Build a model to estimate how long government permitting may take based on historical patterns and other predictive features.

Predicting Construction Costs
Use four years of historical labor and material expense data to predict the expected cost of future construction jobs.

Automating Construction Design
Explore machine learning approaches to help determine things such as where trenches should go, how many poles may be needed, and where cables should be placed.

ResponsibilitiesBuild, deploy, and improve machine learning models.Perform feature engineering on historical and operational data.Investigate model accuracy and identify opportunities for improvement.Build pipelines for recurring model retraining.Develop features that capture historical trends and changing behavior over time.Experiment with different predictive modeling approaches.Evaluate model performance and investigate errors.Work with production ML systems and data pipelines.Collaborate with other ML and engineering team members.Help move new ML concepts from experimentation into production.Required / Desired ExperienceMachine Learning Engineering experience.Predictive modeling experience.Strong Python.Experience with XGBoost, LightGBM, or other boosting algorithms.Feature engineering.Model evaluation.Data pipeline development.Production ML experience.AWS.MLflow or comparable experiment tracking.Strong analytical and problem-solving skills.Technology Stack

Python | AWS | XGBoost | MLflow | DVC | FastAPI | HashiCorp Nomad

GCS is acting as an Employment Business in relation to this vacancy.

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Senior Machine Learning Engineer - Predictive Modeling

We're expanding an ML team focused on applying machine learning to real-world construction and operational problems.

We're looking for a Senior Machine Learning Engineer who can step into an existing production environment, understand how the models work, identify areas for improvement, and help build new predictive capabilities.

The team currently has a production model that predicts construction job duration and is looking to strengthen that model while developing additional models around permits, costs, and automated construction design.

What Makes This Role Interesting?

You won't be starting from a blank page.

There is already a production model performing well across approximately 80% of use cases. Your challenge will be understanding the remaining 20%:

Why are those predictions inaccurate?Is additional feature engineering needed?Are there specific use cases that require a specialized model?What additional historical signals could improve prediction accuracy?How can the model be retrained and kept current?

You'll also have the opportunity to work on new predictive AI problems.

What You'll DoOwn and enhance production machine learning models.Analyze model performance and identify areas for improvement.Develop feature engineering strategies using historical and geographic data.Build automated retraining pipelines.Develop predictive models using algorithms such as XGBoost.Work with time-based and historical trends to improve predictions.Evaluate factors such as county, ZIP code, job type, seasonality, holidays, and historical processing times.Develop models for construction duration and permitting.Contribute to predictive cost modeling using historical labor and material data.Explore ML approaches for automating construction design.Work with engineers and the broader team to productionize ML solutions.What You BringStrong Machine Learning Engineering background.Experience developing predictive models.Strong Python skills.Experience with XGBoost or boosting algorithms.Feature engineering experience.Experience analyzing model performance and improving accuracy.Experience building ML pipelines and production systems.AWS experience.Experience with MLflow, DVC, or similar ML tooling.Strong understanding of supervised machine learning.Team & Technology

The current team includes ML resources in both the U.S. and India and is continuing to grow. The environment is AWS-based and uses HashiCorp Nomad, FastAPI, DVC, MLflow, and Python.

If you're an ML Engineer who likes digging into data, figuring out why a model isn't working, and turning those findings into better production models, we'd love to hear from you.

GCS is acting as an Employment Business in relation to this vacancy.

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Machine Learning Engineer - Production ML/AI

We're hiring a Machine Learning Engineer to help support and expand a growing production ML environment focused on Comcast's construction operations.

This role is ideal for someone who enjoys more than just building models. You'll be responsible for understanding existing models, improving their accuracy, building data/retraining pipelines, and helping move predictive solutions into production.

The team currently has a production model that predicts how long construction jobs will take and is looking to improve the model while expanding into additional predictive use cases.

The Opportunity

You'll work on several machine learning problems, including:

Construction Duration Prediction
Improve an existing production model that predicts how long construction work will take.

Permit Prediction
Develop a model to predict how long it may take to obtain government permits required for construction.

Cost Prediction
Build a predictive model using historical material and labor expenses to estimate the cost of future construction work.

Automated Construction Design
Explore ML approaches that could help automate aspects of construction design, including trench placement, poles, and cable layouts.

Your ResponsibilitiesAnalyze existing production models and identify opportunities for improvement.Build automated model retraining pipelines.Perform feature engineering and analyze historical data.Investigate model errors and develop solutions for underperforming scenarios.Determine when additional features are appropriate versus when a specialized model may be needed.Develop predictive models using structured and historical data.Build data pipelines supporting model training and feature engineering.Track experiments and model performance.Deploy and integrate ML applications into production environments.Preferred BackgroundMachine Learning Engineering experience.Experience with predictive modeling.Strong Python experience.Experience with XGBoost, LightGBM, or similar ML algorithms.Feature engineering and model evaluation.Experience building ML/data pipelines.Production ML / MLOps experience.AWS experience.MLflow or similar experiment-tracking experience.Strong problem-solving skills and the ability to investigate why models succeed or fail.Environment

AWS | Python | XGBoost | MLflow | DVC | FastAPI | HashiCorp Nomad

This is a great opportunity for someone who wants to work on real predictive AI problems with measurable business impact, rather than purely theoretical ML research.

GCS is acting as an Employment Business in relation to this vacancy.

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Machine Learning Engineer - Predictive AI

We're looking for a Machine Learning Engineer to join a team building predictive AI solutions for Comcast's construction operations.

The team already has a production ML model that predicts how long a construction job will take, and the model is currently performing well across the majority of use cases. We're looking for someone who can take ownership of the existing model, improve its performance, and help build the next generation of predictive models.

What You'll Work OnTake ownership of an existing production ML model and improve its performance across challenging use cases.Build and maintain pipelines to regularly retrain models.Analyze the cases where the current model is less accurate and determine whether additional feature engineering or specialized models can improve results.Develop predictive models around construction timelines, permitting, and project costs.Work with historical data to identify patterns that can improve predictions.Engineer features around geography, job characteristics, historical response times, seasonality, holidays, and other factors.Experiment with different modeling approaches, including XGBoost and other machine learning techniques.Track experiments and model performance using MLflow.Work closely with the team to take ML models from development into production.What We're Looking ForStrong hands-on Machine Learning Engineering experience.Experience building and improving predictive models.Strong understanding of feature engineering and model evaluation.Experience with Python and data pipelines.Experience with XGBoost or similar boosting algorithms.Experience working with production ML models.Understanding of model retraining, monitoring, and experimentation.Experience with AWS and modern ML tooling.Ability to investigate model performance and determine why certain predictions are inaccurate.Technology

Python | XGBoost | AWS | MLflow | DVC | FastAPI | HashiCorp Nomad | Machine Learning | Predictive Modeling | Feature Engineering

If you enjoy taking real-world business problems, turning them into predictive models, and then improving those models in production, this could be a great opportunity.

GCS is acting as an Employment Business in relation to this vacancy.

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Senior Software Engineer - Data, Machine Learning & Applications

Job Summary

We are seeking a versatile Senior Software Engineer with experience in application development, databases, data processing, and Machine Learning systems. This role will focus on designing and building robust, scalable technical solutions while ensuring high standards for software quality and performance.

The ideal candidate combines strong software engineering fundamentals with experience working with relational databases, SQL, data solutions, and modern application development technologies.

ResponsibilitiesDesign, develop, test, and maintain scalable software and Machine Learning applications.Write clean, efficient, and well-documented code that meets performance and business requirements.Participate in application architecture, system design, and technical solution development.Design and build relational databases for application and data processing needs.Write and maintain SQL statements for data retrieval, insertion, and processing.Support data acquisition, data warehousing, archival, and recovery strategies.Evaluate data sources and ensure data quality and integration standards are met.Collaborate with customers, business analysts, and technical teams to understand requirements.Develop and execute testing strategies to ensure reliable software delivery.Track defects, test results, and quality metrics.Identify opportunities to improve software processes, application performance, and technical solutions.Participate in code reviews and recommend improvements to existing systems.Support applications throughout the full software development lifecycle.Required QualificationsBachelor's degree in Computer Science, Information Systems, or a related field, or equivalent professional experience.8+ years of experience in software development, systems engineering, testing, data engineering, or related areas.Strong programming experience with technologies such as:Java.NETNode.jsRubyAngularJSStrong SQL and relational database experience.Experience designing, developing, or supporting scalable software applications.Strong object-oriented design skills.Experience with software testing and quality assurance practices.Ability to work across application development, data, and business teams.Excellent analytical and problem-solving skills.Preferred QualificationsExperience building or supporting Machine Learning systems.Experience with data warehousing or large-scale data processing.Experience evaluating and integrating new data sources.Experience with system architecture and application performance optimization.

GCS is acting as an Employment Business in relation to this vacancy.

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Machine Learning Engineer - Advertising & Personalization

Remote (USA)

About the Role

We are seeking a talented and motivated Machine Learning Engineer I to join our growing team. This is an exciting opportunity for an early-career professional who is passionate about building machine learning solutions that create meaningful user experiences at scale.

The ideal candidate enjoys solving complex problems, writing production-quality code, and collaborating with cross-functional teams to develop and deploy intelligent systems. You will work closely with data scientists, software engineers, and product teams to build and optimize machine learning models that drive business impact.

Key ResponsibilitiesDevelop, train, and deploy machine learning models for personalization, recommendations, and customer-focused applications.Build scalable data pipelines for feature engineering, model training, evaluation, and inference.Collaborate with data scientists and software engineers to transition research and prototypes into production systems.Improve recommendation and ranking models using large-scale behavioral and engagement data.Monitor and evaluate model performance through experimentation, A/B testing, and data-driven analysis.Write clean, maintainable, and efficient Python code following software engineering best practices.Support initiatives that enhance customer engagement and business outcomes through machine learning solutions.Required QualificationsBachelor's or Master's degree in Computer Science, Machine Learning, Artificial Intelligence, Statistics, Mathematics, Engineering, or a related quantitative field.1-3 years of relevant experience, including internships, research, academic projects, or professional experience.Strong programming skills in Python.Solid understanding of machine learning fundamentals, including:Supervised learningClassification and regressionFeature engineeringModel evaluation and validationExperience with machine learning frameworks such as Scikit-learn, TensorFlow, or PyTorch.Experience working with SQL and large datasets.Familiarity with Git and software development best practices.Strong analytical and problem-solving skills.Why Join Us?Work on machine learning products that impact large-scale user experiences.Collaborate with highly skilled engineers, data scientists, and researchers.Gain hands-on experience deploying production-grade machine learning solutions.Grow your expertise in modern ML technologies and scalable systems.Flexible remote work environment with opportunities for learning and career development.

GCS is acting as an Employment Business in relation to this vacancy.

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