ML Platform Engineer – Autonomous Driving

42dot

ML Platform Engineer at 42dot developing scalable data platforms for autonomous driving. Building infrastructure to support ML model training, evaluation, and deployment while collaborating with cross-functional teams.

Posted 7/20/2026full-timePangyo • 🇰🇷 South KoreaMid-LevelSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in developing and maintaining high-scale data platforms for autonomous driving, with a strong focus on data processing pipelines, machine learning integration, and data governance. Proficient in Python SDK development and experienced with big data technologies and architectures.

Highest-signal resume keywords

Python SDK DevelopmentData Pipeline OrchestrationData Warehouse TechnologiesAutonomous Vehicle Sensor DataMachine Learning Model Training Lifecycle

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Applicant Tracking System Keywords

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Hard Skills

Data EngineeringMachine Learning PlatformsPythonDatabasesDatabricks WorkflowsApache AirflowApache SparkData WarehouseDelta LakeAI Frameworks

Tools & Technologies

MongoDBPostgreSQLHiveLiDARCameraRadarPyTorchTensorFlow

Industry Keywords

Data GovernanceData Privacy RegulationsData Processing PipelinesData LakehouseAutonomous Driving

Tech Stack

Tools & technologies

AirflowApacheBootstrapCloudMongoDBPostgresPythonPyTorchSparkTensorflow

About the role

Key responsibilities & impact

Develop a high scale, reliable data platform to manage, visualize, search and serve large-scale datasets for ML model training, fine tune and validation.
Develop advanced autonomous driving data SDK, including scene data search, datasets preparation, dataset loading, etc.
Build up the data lakehouse for autonomous driving scene dataset, including the sensor data, calibration data, as well as annotation data
Dig into performance bottlenecks all along the data processing pipelines, from data processing latency, data search latency to Test Procedure (TP) coverage.
Bootstrap and maintain infrastructure for data platform components—data processing pipeline, database, data lakehouse and data serving.
Collaborate with cross-functional teams, including ML algorithm, ML application, and Cloud Infra to align ML Platforms with overall autonomous driving system architecture.

Requirements

What you’ll need

Bachelor's degree or higher in Computer Science, Engineering, Robotics, or a similar technical field.
Minimum of 5 years of experience in Data Engineering or ML Platform roles
Proficient in Python and solid experience in Python SDK development
Solid working experience in Databases (e.g., MongoDB, PostgreSQL, etc)
Hands-on experience with data pipeline job orchestration with Databricks Workflows or Apache Airflow, as well as integrating data pipelines with machine learning models
Extensive experience with data technologies and architectures such as Data Warehouse (e.g., Hive) or Lakehouse (e.g., Delta Lake)
Experience with Apache Spark or other big data computing engines
Experience with autonomous vehicle sensor data (e.g., LiDAR, camera, radar)
Experience with ML model training lifecycle (e.g., data preparation, model training / validation / deployment, etc)
Understanding of modern AI frameworks (e.g., PyTorch, TensorFlow etc.)
Understanding data governance principles, data privacy regulations, and experience implementing security measures to protect data

Benefits

Comp & perks

The recruitment process may change depending on schedule and progress; the result of each stage will be sent individually to your registered email.
For application errors or inquiries, contact recruit@42dot.ai.
Veterans and applicants eligible for employment protection will receive preferential consideration in accordance with applicable laws and regulations.
In compliance with the Act on Employment Promotion and Vocational Rehabilitation for Persons with Disabilities, registered individuals with disabilities will receive preferential consideration.
42dot does not accept unsolicited resumes from search firms. We will not pay any fees for resumes submitted without prior agreement.
False information in your application may result in offer cancellation.
A reference check may be conducted after the interview process, with your consent.
A 3-month probationary period may apply.