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Contract Audio Machine Learning Jobs in Roselle, IL

Varsity Tutors

Data Science Tutor

Highland Park, IL · Remote

$18 - $40/hr

Deep knowledge of statistical analysis, data wrangling, exploratory data analysis, machine learning ... Varsity Tutors does not contract in: Alaska, California, Colorado, Delaware, Hawaii, Maine, New ...

Varsity Tutors

Data Science Tutor

Evanston, IL · Remote

$18 - $40/hr

Deep knowledge of statistical analysis, data wrangling, exploratory data analysis, machine learning ... Varsity Tutors does not contract in: Alaska, California, Colorado, Delaware, Hawaii, Maine, New ...

Varsity Tutors

Data Science Tutor

Oak Lawn, IL · Remote

$18 - $40/hr

Deep knowledge of statistical analysis, data wrangling, exploratory data analysis, machine learning ... Varsity Tutors does not contract in: Alaska, California, Colorado, Delaware, Hawaii, Maine, New ...

Varsity Tutors

Data Science Tutor

Lake Forest, IL · Remote

$18 - $40/hr

Deep knowledge of statistical analysis, data wrangling, exploratory data analysis, machine learning ... Varsity Tutors does not contract in: Alaska, California, Colorado, Delaware, Hawaii, Maine, New ...

Varsity Tutors

Data Science Tutor

Buffalo Grove, IL · Remote

$18 - $40/hr

Deep knowledge of statistical analysis, data wrangling, exploratory data analysis, machine learning ... Varsity Tutors does not contract in: Alaska, California, Colorado, Delaware, Hawaii, Maine, New ...

Varsity Tutors

Data Science Tutor

Des Plaines, IL · Remote

$18 - $40/hr

Deep knowledge of statistical analysis, data wrangling, exploratory data analysis, machine learning ... Varsity Tutors does not contract in: Alaska, California, Colorado, Delaware, Hawaii, Maine, New ...

Varsity Tutors

Data Science Tutor

Wheaton, IL · Remote

$18 - $40/hr

Deep knowledge of statistical analysis, data wrangling, exploratory data analysis, machine learning ... Varsity Tutors does not contract in: Alaska, California, Colorado, Delaware, Hawaii, Maine, New ...

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Showing results 41-60

All JobsContract Audio Machine Learning JobsContract Audio Machine Learning Jobs in Roselle, IL

Contract Audio Machine Learning information

See Roselle, IL salary details

$30

$49

$99

How much do contract audio machine learning jobs pay per hour?

As of Aug 19, 2026, the average hourly pay for contract audio machine learning in Roselle, IL is $49.12, according to ZipRecruiter salary data. Most workers in this role earn between $41.25 and $50.91 per hour, depending on experience, location, and employer.

What is the difference between Contract Audio Machine Learning vs Contract Data Scientist?

AspectContract Audio Machine LearningContract Data Scientist
Required CredentialsDegree in Computer Science, Data Science, or related field; experience with machine learning frameworksDegree in Data Science, Statistics, or related; strong programming skills
Work EnvironmentFocus on audio data, signal processing, and machine learning modelsBroader data analysis, statistical modeling, and data visualization
Industry UsageMedia, entertainment, speech recognition, audio analysisFinance, healthcare, marketing, and various industries requiring data insights

Contract Audio Machine Learning specialists focus on developing models specifically for audio data, while Contract Data Scientists handle a wider range of data types and analysis tasks. Both roles require strong technical skills, but their focus areas and industry applications differ.

What are popular job titles related to Contract Audio Machine Learning jobs in Roselle, IL?

For Contract Audio Machine Learning jobs in Roselle, IL, the most frequently searched job titles are:

What job categories do people searching Contract Audio Machine Learning jobs in Roselle, IL look for?

The top searched job categories for Contract Audio Machine Learning jobs in Roselle, IL are:

What cities near Roselle, IL are hiring for Contract Audio Machine Learning jobs?

Cities near Roselle, IL with the most Contract Audio Machine Learning job openings:

Contract Audio Machine Learning jobs near you

Google Cloud Platform AI/ML Engineer

CoSourcing Partners

Chicago, IL • On-site

Other

Posted 5 days ago

Job description

Job Title: Google Cloud Platform AI/ML Engineer
Duration: 6 months Contract to hire
Location: Chicago is the preferred location, but open to candidates from anywhere in the U.S.
Role Overview
We are seeking a talented and experienced Google Cloud Platform AI/ML Engineer to design, build, and operationalize scalable machine learning solutions on Google Cloud Platform (Google Cloud Platform). This role focuses on developing production-grade ML pipelines, automating workflows, and ensuring reliability and governance across enterprise AI platforms.
The ideal candidate will have strong expertise in Vertex AI, MLOps, and cloud-native ML architectures, with a passion for turning data science models into scalable, production-ready systems.
Key Responsibilities
ML Pipeline Development & Automation
  • Build, deploy, and manage production-grade machine learning pipelines using Vertex AI Pipelines and Google Cloud Platform-native services.
  • Design automated workflows for data ingestion, feature engineering, model training, evaluation, and inference.
  • Orchestrate ML workflows using Python, Vertex AI, BigQuery, and Cloud Storage.
  • Ensure pipelines are modular, reusable, and scalable across use cases.

Model Operationalization (MLOps)
  • Operationalize the end-to-end ML lifecycle, including:
  • Model training
  • Deployment
  • Monitoring
  • Retraining and lifecycle management
  • Deploy models using Vertex AI endpoints with support for online and batch predictions.
  • Implement robust CI/CD pipelines for ML artifacts and workflows.
  • Enable automated model retraining and versioning strategies.

Data Integration & Feature Engineering
  • Enable seamless data flows across data lakes, warehouses, and ML platforms.
  • Design and manage feature pipelines for training and inference datasets.
  • Integrate with BigQuery, Cloud Storage, and streaming sources to support real-time and batch ML use cases.
  • Ensure consistency between training and serving data pipelines.

Model Monitoring & Performance Optimization
  • Implement model monitoring solutions to track:
  • Prediction accuracy
  • Data drift and concept drift
  • Model performance degradation
  • Set up alerting mechanisms and dashboards for proactive issue detection.
  • Optimize model performance and infrastructure for scalability, latency, and cost efficiency.

AI Platform Engineering
  • Build and enhance enterprise AI/ML platforms with a focus on:
  • Automation
  • Observability
  • Reliability
  • Develop standardized frameworks for repeatable and governed ML deployments.
  • Establish best practices for MLOps, pipeline orchestration, and infrastructure management.

Collaboration & Cross-Functional Engagement
  • Collaborate closely with:
  • Data Scientists to productionize models
  • Data Engineers for data pipeline integration
  • Architects for scalable cloud designs
  • Translate business requirements into deployable ML solutions.
  • Provide technical leadership and mentoring on ML engineering practices.

Governance, Security & Best Practices
  • Implement model governance frameworks including auditability, lineage, and compliance.
  • Ensure secure handling of data and models using IAM roles and access policies.
  • Promote best practices in:
    • Code versioning (Git)
    • CI/CD
    • Testing and validation
  • Drive documentation and standardization across ML workflows.

Required Qualifications
  • Bachelor's or Master's degree in Computer Science, Data Science, Engineering, or related field.
  • 4+ years of experience in machine learning engineering or MLOps.
  • Hands-on experience with Google Cloud Platform (Google Cloud Platform) services:
  • Vertex AI (Pipelines, Training, Endpoints)
    oBigQuery
    oCloud Storage
  • Strong programming skills in Python.
  • Experience building and deploying end-to-end ML pipelines.
  • Strong understanding of ML lifecycle and MLOps principles.

Preferred Skills
  • Experience with TensorFlow, PyTorch, or Scikit-learn.
  • Familiarity with Kubeflow Pipelines or Apache Beam.
  • Experience with Docker and containerized deployments.
  • Knowledge of real-time ML inference and streaming architectures.
  • Hands-on experience with model monitoring tools and frameworks.
  • Understanding of feature stores and feature engineering pipelines.

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