Machine Learning Data Engineer — Computer Vision & Datasets

Funstop Games; Turkey; On-site; TRY 350,000 - 700,000; Full time

Job summary

Funstop Games is seeking a Machine Learning Data Engineer to build and maintain data pipelines and infrastructure that power our computer vision and ML initiatives. You will manage large-scale visual datasets, automate data preparation workflows, and ensure our AI engineers have access to reliable, well-structured, high-quality training data.

You will collaborate with AI engineers, annotation specialists, and infrastructure teams to improve efficiency, quality, and reproducibility of the ML

Qualifications

• 3+ years of professional experience in data engineering, data infrastructure, or related roles.
• Strong proficiency in Python and SQL.
• Experience building and maintaining ETL/ELT pipelines and automated data processing workflows.
• Experience with large-scale datasets, cloud storage, databases, and APIs.
• Understanding of data validation, cleaning, transformation, and data integrity principles.
• Familiarity with image data, computer vision datasets, and common dataset formats.
• Experience with Git, Linux, Docker, and software engineering best practices.
• Familiarity with cloud platforms such as AWS, Google Cloud, or Azure.
• Strong analytical and problem-solving skills, with attention to data quality and reliability.
• Professional working proficiency in English.

Responsibilities

• Dataset Engineering: Build and maintain scalable pipelines for collecting, processing, organizing, and preparing image datasets for training, validation, and evaluation.
• Data Quality & Validation: Develop automated checks for corrupted images, duplicates, missing metadata, inconsistent labels, and class imbalance.
• Data Versioning & Traceability: Implement dataset versioning, lineage tracking, metadata management, and reproducible data preparation workflows.
• Annotation Workflows: Integrate annotation tools with storage systems and ML pipelines. Automate label import, export, validation, and review processes.
• Data Infrastructure & Automation: Develop ETL/ELT pipelines and automated processing jobs using appropriate orchestration and distributed processing tools.
• Performance & Reliability: Optimize data processing speed, storage efficiency, and infrastructure costs. Implement monitoring, logging, error handling, and recovery mechanisms.
• Cross-functional Collaboration: Work with ML engineers to translate model training requirements into reliable data workflows and maintain clear documentation of datasets and transformations.

Skills

Python

SQL

ETL pipelines

Data quality

OpenCV

NumPy

Pandas

Git

Linux

Docker

English

3+ years experience

Cloud platforms

Education

Bachelor's degree in Computer Science or related field

Tools

Git

Linux

Docker

OpenCV

Pillow

NumPy

Pandas

CVAT

Label Studio

FiftyOne

DVC

LakeFS

Airflow

Prefect

Dagster

AWS

Google Cloud

Azure

Job description

We're looking for a Machine Learning Data Engineer to build and maintain the data pipelines and infrastructure that power our computer vision and machine learning initiatives.

In this role, you'll manage large-scale visual datasets, automate data preparation workflows, and ensure our AI engineers have access to reliable, well-structured, and high-quality training data. You'll work closely with AI engineers, annotation specialists, and infrastructure teams to improve the efficiency, quality, and reproducibility of our ML development process.

The ideal candidate has strong data engineering fundamentals along with practical experience handling image datasets, data annotation workflows, and machine learning data preparation.

Key Responsibilities

• Dataset Engineering: Build and maintain scalable pipelines for collecting, processing, organizing, and preparing image datasets for training, validation, and evaluation.
• Data Quality & Validation: Develop automated checks for corrupted images, duplicates, missing metadata, inconsistent labels, and class imbalance.
• Data Versioning & Traceability: Implement dataset versioning, lineage tracking, metadata management, and reproducible data preparation workflows.
• Annotation Workflows: Integrate annotation tools with storage systems and ML pipelines. Automate label import, export, validation, and review processes.
• Data Infrastructure & Automation: Develop ETL/ELT pipelines and automated processing jobs using appropriate orchestration and distributed processing tools.
• Performance & Reliability: Optimize data processing speed, storage efficiency, and infrastructure costs. Implement monitoring, logging, error handling, and recovery mechanisms.
• Cross-functional Collaboration: Work with ML engineers to translate model training requirements into reliable data workflows and maintain clear documentation of datasets and transformations.

Required Qualifications

• Bachelor's degree in Computer Science, Data Engineering, Software Engineering, or a related field, or equivalent practical experience.
• 3+ years of professional experience in data engineering, data infrastructure, or related engineering roles.
• Strong proficiency in Python and SQL.
• Experience building and maintaining ETL/ELT pipelines and automated data processing workflows.
• Experience working with large-scale datasets, cloud storage, databases, and APIs.
• Understanding of data validation, cleaning, transformation, and data integrity principles.
• Familiarity with image data, computer vision datasets, and common dataset formats.
• Experience with Git, Linux, Docker, and software engineering best practices.
• Familiarity with cloud platforms such as AWS, Google Cloud, or Azure.
• Strong analytical and problem-solving skills, with attention to data quality and reliability.
• Professional working proficiency in English.

Preferred Qualifications

• Experience managing large-scale image datasets and computer vision annotation workflows.
• Familiarity with OpenCV, Pillow, NumPy, and Pandas.
• Experience with CVAT, Label Studio, FiftyOne, or similar dataset and annotation tools.
• Knowledge of dataset versioning tools such as DVC or lakeFS.
• Experience with workflow orchestration tools such as Airflow, Prefect, or Dagster.
• Experience with image deduplication, metadata indexing, and dataset quality monitoring.
• Understanding of PyTorch dataset loaders and ML training data requirements.
• Experience with cloud object storage, such as Amazon S3 or Google Cloud Storage.
• Familiarity with MLOps practices and ML experiment tracking.

Interested? We'd love to hear about the data pipelines you've built, the datasets you've managed, and the engineering challenges you've solved.

At Funstop Games, we celebrate diversity and are committed to creating an inclusive environment where everyone, regardless of background, culture, or identity, has an equal opportunity to thrive. Different perspectives make our team stronger—and our games better.