Machine Learning Engineer – Internship

Unity

. Build and maintain data pipelines that generate training datasets for machine learning models and experimentation .

Posted 9/23/2026full-timeRemote • United StatesEntry Level💰 $117,000 - $152,000 per yearWebsite

Core Competencies

Role fit

Core Competencies

Use this summary to align your resume positioning with the role.

Demonstrates expertise in building and maintaining data pipelines for machine learning, with a strong foundation in distributed systems and large-scale data processing. Proficient in optimizing performance and reliability of machine learning infrastructure using tools like PyTorch, Ray, and workflow orchestration systems.

Highest-signal resume keywords

Data Pipeline DevelopmentMachine Learning SystemsPython ProgrammingWorkflow Orchestration (Airflow, Flyte)Distributed Systems (Ray, Spark)

ATS Keywords

Tailor your resume

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills

Data Pipeline DevelopmentMachine Learning SystemsPython ProgrammingDistributed SystemsData ProcessingDataset ValidationModel Training WorkflowsLarge DatasetsPerformance OptimizationReliability Improvement

Soft Skills

Problem-SolvingCollaboration

Tools & Technologies

PyTorchRayAirflowFlyteTensorFlowData LakesData WarehousesStreaming Systems

Industry Keywords

Machine LearningDistributed SystemsLarge-Scale Data ProcessingScalable InfrastructureResearch Publications

Tech Stack

Tools & technologies

AirflowDistributed SystemsPythonPyTorchRaySparkTensorflow

About the role

Key responsibilities & impact

Build and maintain data pipelines that generate training datasets for machine learning models and experimentation
Contribute to infrastructure supporting distributed training workflows using tools such as PyTorch and Ray
Work with workflow orchestration tools such as Airflow and Flyte to support multi-stage ML pipelines
Improve reproducibility and reliability through dataset validation, monitoring, and testing
Partner with ML engineers to support experimentation and model iteration
Optimize performance and efficiency across data processing and training systems
Contribute to the evolution of the offline ML platform architecture as it scales

Requirements

What you’ll need

Bachelor’s degree in Computer Science, Machine Learning, Systems, or a related field
Strong foundation in machine learning systems, distributed systems, or large-scale data processing through research or projects
Experience with Python and data-intensive workloads
Familiarity with ML frameworks such as PyTorch and TensorFlow and/or distributed systems such as Ray and Spark
Academic or applied experience with data pipelines, model training workflows, or large datasets
Strong problem-solving skills and ability to translate research ideas into practical systems
Interest in building scalable, reliable machine learning infrastructure
English proficiency sufficient for professional verbal and written exchanges
Nice to have: experience with workflow orchestration systems such as Airflow or Flyte
Nice to have: exposure to large-scale data platforms, including data lakes, warehouses, and streaming systems
Nice to have: publications or research in ML systems, distributed systems, or related areas

Benefits

Comp & perks

Equity awards
Participation in company incentive plans, such as annual discretionary bonuses or sales commissions
Comprehensive health, life, and disability insurance
Commute subsidy
Employee stock ownership
Competitive retirement/pension plans
Generous vacation and personal days
New-parent leave and family-care programs
Office food snacks
Mental Health and Wellbeing programs and support
Employee Resource Groups
Global Employee Assistance Program
Training and development programs
Volunteering and donation matching program