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Trainee Mechanical Engineering Machine Learning Jobs

SynergisticIT

Machine Learning Engineer

Boston, MA · On-site

Work with an international top-notch engineering team with full commitment on Machine Learning ... Mechanical Engineering. Benefits of Working with Our Clients * E-Verified. * Long Term Positions

Atomic Machines

Mechanical Engineer

Santa Clara, CA

The Mechanical Engineering team is at the forefront of designing and developing state-of-the-art ... Drive your work through rapid learning loops leveraging our in-house Lab, Fab, and Machine Shop.

Atomic Machines

Mechanical Engineer

Emeryville, CA

The Mechanical Engineering team is at the forefront of designing and developing state-of-the-art ... Drive your work through rapid learning loops leveraging our in-house Lab, Fab, and Machine Shop.

SynergisticIT

Machine Learning Engineer

Charleston, SC · On-site

Work with an international top-notch engineering team with full commitment on Machine Learning ... Mechanical Engineering. Benefits of Working with Our Clients * E-Verified. * Filing of H1b and ...

Apple

Machine Learning Engineer - LLM

Cupertino, CA · On-site

$147K - $220K/yr

Masters in Machine Learning, Artificial Intelligence, Computer Science, Statistics, Operations Research, Physics, Mechanical Engineering, Electrical Engineering or related field. * Ability to travel ...

HADRIAN

Machine Learning Engineer

Los Angeles, CA

$250K/yr

As a Senior Machine Learning Engineer, you will play a key role in designing, building, and scaling ... Bachelors in Computer Science, Electrical Engineering, Mechanical Engineering (or similar ...

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How much do trainee mechanical engineering machine learning jobs pay per hour?

As of Jun 23, 2026, the average hourly pay for trainee mechanical engineering machine learning in the United States is $33.20, according to ZipRecruiter salary data. Most workers in this role earn between $26.92 and $37.50 per hour, depending on experience, location, and employer.
What cities are hiring for Trainee Mechanical Engineering Machine Learning jobs? Cities with the most Trainee Mechanical Engineering Machine Learning job openings:
What are the most commonly searched types of Mechanical Engineering Machine Learning jobs? The most popular types of Mechanical Engineering Machine Learning jobs are:
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Trainee Mechanical Engineering Machine Learning jobs near you
Machine Learning Engineer

Machine Learning Engineer

Darwill, Inc.

Oakbrook Terrace, IL • On-site

Full-time

Posted 3 days ago

Job description

MACHINE LEARNING ENGINEER (MLOPS / DATA ENGINEERING)
Overview
Darwill is a nationally recognized print and marketing communications firm based in the west suburbs of Chicago. As a premier provider of complex, data-driven marketing solutions, we help CMOs and marketing leaders drive measurable performance through advanced analytics, automation, and AI-powered insights.
We are seeking a Machine Learning Engineer (MLOps) to support the productionization of traditional machine learning models (e.g., propensity and segmentation models) while also building and maintaining the core data pipelines on Databricks that power our analytics and modeling platforms.
This role is intentionally scoped for a mid-level engineer: someone with enough experience to work independently and make sound engineering decisions, but who is still hands-on, execution-focused, and eager to grow. This is not an entry-level position, and it is not a principal or architect-level role..
Location
Chicago, IL area (Oak Brook / West Suburbs)
Hybrid work model with 1-2 days onsite per week required
Reports To
VP of Data Engineering & Data Science
Responsibilities / Essential Functions
Data Engineering & Platform Foundations
  • Design, build, and maintain ETL pipelines in Databricks using Spark and Delta Lake
  • Independently implement data transformations, joins, and aggregations across large, multi-source datasets
  • Build and maintain data validation and quality checks to ensure reliability of downstream analytics and ML workflows
  • Optimize Databricks jobs for performance, scalability, and cost efficiency
  • Write and maintain clear technical documentation for data pipelines and tables

ML Engineering & MLOps
  • Partner closely with Data Scientists to support traditional ML model development, including feature engineering, training, validation, and deployment
  • Productionize propensity, ranking, and segmentation models used in large-scale marketing campaigns
  • Build and maintain repeatable ML pipelines for training, batch scoring, and inference
  • Implement model versioning, experiment tracking, and reproducibility standards
  • Support model performance monitoring, drift detection, and retraining cycles

Deployment, Monitoring & Operations
  • Deploy data pipelines and ML workflows into production environments serving millions of records
  • Implement monitoring and alerting for data and ML pipelines
  • Support A/B testing and model performance evaluation in partnership with Data Science
  • Troubleshoot production issues independently and collaborate effectively when escalation is needed

GenAI (Secondary / Directional)
  • Contribute to GenAI initiatives as capacity allows
  • Stay informed on emerging AI technologies and tooling
    (GenAI is not the primary focus of this role today.)

Required Qualifications
Experience
  • 3-6 years of professional experience in machine learning engineering, data engineering, or a closely related role
  • Experience working in production environments with minimal day-to-day supervision
  • Demonstrated ability to collaborate effectively with Data Scientists and translate models into production systems

Technical Skills (Must-Have)
Data Engineering & Platform
  • Apache Spark (PySpark, SparkSQL)
  • Databricks (ETL pipelines, workflows, Delta Lake)
  • Strong SQL skills (complex queries, joins, window functions, optimization)
  • Experience building and maintaining scalable data pipelines

Programming & Machine Learning
  • Python (pandas, numpy, scikit-learn; experience with XGBoost or LightGBM preferred)
  • Feature engineering and data preparation for ML models
  • Working knowledge of supervised learning models (classification, regression, ranking)

MLOps & Production
  • Experience deploying ML models into production
  • Model versioning and experiment tracking (e.g., MLflow or similar)
  • Monitoring data quality and model performance in production
  • Supporting retraining and validation workflows

Cloud & Tooling
  • Experience with a major cloud platform (Databrick, AWS)
  • Familiarity with workflow orchestration tools (Databricks Workflows or similar)

Preferred Qualifications (Nice-to-Have)
  • Experience with propensity modeling, customer segmentation, or marketing analytics
  • Exposure to CI/CD concepts for data and ML pipelines
  • Experience with Docker or containerized deployments
  • Exposure to GenAI, LLMs, or RAG-based systems
  • Master's degree in Computer Science, Statistics, or a related field

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