📍 Job Overview

Job Title: Senior ML Platform Engineer
Company: 42dot
Location: Pangyo, Gyeonggi-do, South Korea
Job Type: On-site
Category: DevOps, Machine Learning
Date Posted: 2025-06-26
Experience Level: 5-10 years
Remote Status: On-site

🚀 Role Summary

Lead the development of a high-scale, reliable data platform for managing, visualizing, and serving large-scale datasets for ML model training and validation.
Collaborate with cross-functional teams to align ML Platforms with the overall Autonomous Driving System Architecture.
Drive the Autonomous Driving Data SDK development, including scene data search, datasets preparation, and dataset loading.
📝 Enhancement Note: This role requires a strong background in data engineering, machine learning platforms, and cross-functional team collaboration to succeed in a dynamic, cutting-edge environment.

💻 Primary Responsibilities

📝 Enhancement Note: The following responsibilities highlight the technical aspects of the role, with a focus on data management, platform development, and team collaboration.
Data Platform Development: Set technical strategy and oversee the development of a high-scale, reliable data platform to manage, visualize, and serve large-scale datasets for ML model training and validation.
Data Lakehouse & SDK Development: Build up the data lakehouse for autonomous driving scene datasets and drive the Autonomous Driving Data SDK development.
Performance Optimization: Dig into performance bottlenecks along the data processing pipelines, from data processing latency, data search latency to Test Procedure (TP) coverage.
Infrastructure Management: Bootstrap and maintain infrastructure for Data Platform components, including Data Processing Pipeline, Database, Data Lakehouse, and Data Serving.
Cross-Functional Collaboration: Collaborate with cross-functional teams, including ML algorithm, ML application, and Cloud Infra, to align ML Platforms with the overall Autonomous Driving System Architecture.

🎓 Skills & Qualifications

Education & Experience

Bachelor's degree or higher in Computer Science, Engineering, Robotics, or a similar technical field.
Minimum of 7 years of experience in Data Engineering or ML Platform roles.

Required Skills

Programming & Data Management: Expert-level proficiency in Python and solid experience in Python SDK development, Databases (e.g., MongoDB, PostgreSQL, etc.), and modern AI frameworks (e.g., PyTorch, TensorFlow etc.).
Data Processing & Orchestration: Hands-on experience with data pipeline job orchestration using Databricks Workflows or Apache Airflow, and integrating data pipelines with machine learning models.
Data Technologies & Architecture: Extensive experience with data technologies and architectures such as Data Warehouse (e.g., Hive) or Lakehouse (e.g., Delta Lake) and Apache Spark or other big data computing engines.
Leadership & Communication: Excellent leadership and communication skills, with a demonstrated ability to lead technical projects.

Preferred Skills

Experience with autonomous vehicle sensor data (e.g., LiDAR, camera, radar).
Understanding of ML model training lifecycle and data governance principles.
Knowledge of Large Models, like VLM.

📊 Web Portfolio & Project Requirements

📝 Enhancement Note: While not explicitly stated, showcasing relevant data engineering and ML platform projects, along with strong problem-solving skills and performance optimization examples, will be crucial for this role.
Data Platform & SDK Projects: Highlight projects demonstrating data platform development, data lakehouse creation, and Autonomous Driving Data SDK development.
Performance Optimization: Include case studies showcasing performance improvements in data processing pipelines and ML model training/evaluation.
Cross-Functional Collaboration: Emphasize projects that involve working with multiple teams, such as ML algorithms, ML applications, and cloud infrastructure.

💵 Compensation & Benefits

Salary Range: Based on industry standards for senior data engineering and ML platform roles in South Korea, the estimated salary range is approximately ₩80,000,000 - ₩120,000,000 per year. 📝 Enhancement Note: This estimate is based on regional market research and web technology industry benchmarks.

Benefits: Although not explicitly stated, common benefits for senior roles in South Korea may include:

Health insurance and retirement plans
Performance-based bonuses and stock options
Flexible working hours and remote work opportunities
Professional development opportunities, such as training, conferences, and certifications

Working Hours: The typical working hours are 40 hours per week, with flexibility for project deadlines and maintenance windows.

🎯 Team & Company Context

🏢 Company Culture

Industry: 42dot is a cutting-edge autonomous driving technology company, focusing on developing advanced AI algorithms and data platforms for autonomous vehicles.

Company Size: As a growing startup, 42dot offers a dynamic and collaborative work environment, with opportunities for significant growth and impact.

Founded: 42dot was founded in 2017 and has since grown to become a leading player in the autonomous driving technology space.

Team Structure: The team consists of experts in various fields, including ML algorithms, ML applications, cloud infrastructure, and data engineering. The data engineering team works closely with other teams to ensure seamless integration and alignment with the overall Autonomous Driving System Architecture.

Development Methodology: 42dot follows Agile development methodologies, with a focus on continuous integration, collaboration, and improvement.

Company Website: 42dot.ai

📈 Career & Growth Analysis

Web Technology Career Level: This role is at the senior level, with a focus on technical leadership, strategic decision-making, and cross-functional collaboration.

Reporting Structure: The Senior ML Platform Engineer reports directly to the Head of Data Engineering and collaborates with various teams, including ML algorithms, ML applications, and cloud infrastructure.

Technical Impact: This role has a significant impact on the overall Autonomous Driving System Architecture, ensuring that data platforms are reliable, scalable, and optimized for ML model training and evaluation.

Growth Opportunities:

Technical Growth: As a senior role, there is ample opportunity for technical growth, including expanding expertise in data engineering, ML platforms, and emerging technologies.
Leadership Development: This role offers opportunities to develop leadership skills, mentoring junior team members, and driving technical projects.
Architecture & System Design: The Senior ML Platform Engineer can expect to make critical architecture decisions, shaping the future of 42dot's data platforms.

🌐 Work Environment

Office Type: 42dot's office is a modern, collaborative workspace designed to foster innovation and teamwork.

Office Location(s): Pangyo, Gyeonggi-do, South Korea

Workspace Context:

Collaboration: The workspace encourages cross-functional collaboration, with open-plan offices and dedicated team spaces.
Work Arrangement: The work arrangement is on-site, with flexible working hours and remote work opportunities for specific projects or roles.

Work Schedule: The work schedule is typically 40 hours per week, with flexibility for project deadlines and maintenance windows.

📄 Application & Technical Interview Process

Interview Process:

Resume Screening: A review of the candidate's resume and portfolio to assess relevant skills and experience.
Coding Test: A technical assessment focusing on data engineering, ML platforms, and problem-solving skills.
Phone/Skype Screen: A brief conversation to discuss the candidate's background, motivation, and cultural fit.
On-site/Video Interview: A comprehensive interview covering technical skills, leadership abilities, and alignment with 42dot's mission and values.

Portfolio Review Tips:

Data Platform & SDK Projects: Highlight projects demonstrating data platform development, data lakehouse creation, and Autonomous Driving Data SDK development.
Performance Optimization: Include case studies showcasing performance improvements in data processing pipelines and ML model training/evaluation.
Cross-Functional Collaboration: Emphasize projects that involve working with multiple teams, such as ML algorithms, ML applications, and cloud infrastructure.

Technical Challenge Preparation:

Brush up on data engineering, ML platforms, and relevant programming languages (e.g., Python, SQL, etc.).
Review data processing techniques, performance optimization strategies, and ML model training/evaluation best practices.
Prepare for system design and architecture discussions, focusing on scalability, reliability, and efficiency.

ATS Keywords: Python, Data Engineering, ML Platform, Databases, AI Frameworks, Data Pipeline, Databricks, Apache Airflow, Data Warehouse, Lakehouse, Apache Spark, Leadership, Communication, Data Governance, Data Privacy, Large Models

🛠 Technology Stack & Web Infrastructure

Programming Languages:

Python (Expert-level proficiency required)

Data Management & Processing:

Databases: MongoDB, PostgreSQL, etc. (Solid working experience required)
Data Warehouse/Lakehouse: Hive, Delta Lake (Extensive experience required)
Big Data Computing Engines: Apache Spark (Experience required)

Data Pipeline & Orchestration:

Databricks Workflows, Apache Airflow (Hands-on experience required)

AI Frameworks:

PyTorch, TensorFlow (Strong understanding required, especially the principle of distributed data loader for model training)

Infrastructure & Cloud:

Cloud Infrastructure: Not explicitly stated, but experience with cloud platforms (e.g., AWS, GCP, Azure) may be beneficial.

👥 Team Culture & Values

Web Development Values:

Innovation: 42dot values innovation and encourages team members to explore new technologies and approaches to data engineering and ML platforms.
Collaboration: The company emphasizes cross-functional collaboration, with a focus on working together to achieve common goals.
Quality & Reliability: 42dot prioritizes high-quality, reliable data platforms that support efficient ML model training and evaluation.
Continuous Learning: The company fosters a culture of continuous learning, with regular training, workshops, and knowledge-sharing sessions.

Collaboration Style:

Cross-Functional Integration: 42dot encourages close collaboration between data engineering, ML algorithms, ML applications, and cloud infrastructure teams.
Code Review & Knowledge Sharing: The company values code reviews and knowledge-sharing practices, with a focus on maintaining high coding standards and best practices.
Mentoring & Technical Growth: 42dot offers mentoring opportunities, with senior team members providing guidance and support to junior team members.

⚡ Challenges & Growth Opportunities

Technical Challenges:

Performance Optimization: Address performance bottlenecks in data processing pipelines, data search latency, and Test Procedure (TP) coverage.
Data Governance & Privacy: Implement data governance principles and ensure compliance with data privacy regulations.
Emerging Technologies: Stay up-to-date with emerging data engineering and ML platform technologies, and integrate them into 42dot's data platforms.

Learning & Development Opportunities:

Technical Skill Development: Expand expertise in data engineering, ML platforms, and emerging technologies through workshops, training, and online courses.
Conference Attendance: Attend industry conferences and events to network with peers, learn about emerging trends, and share best practices.
Technical Mentorship: Provide mentorship to junior team members, fostering a culture of continuous learning and growth.

💡 Interview Preparation

Technical Questions:

Data Engineering & ML Platforms: Prepare for technical questions focusing on data engineering, ML platforms, and performance optimization strategies.
System Design & Architecture: Brush up on system design principles, with a focus on scalability, reliability, and efficiency.
Problem-Solving: Review problem-solving techniques and be prepared to discuss real-world data engineering and ML platform challenges.

Company & Culture Questions:

Mission & Values: Familiarize yourself with 42dot's mission and values, and be prepared to discuss how your background and skills align with the company's goals.
Team Dynamics: Research 42dot's team structure and dynamics, and be prepared to discuss how you would collaborate with various teams, such as ML algorithms, ML applications, and cloud infrastructure.

Portfolio Presentation Strategy:

Data Platform & SDK Projects: Highlight projects demonstrating data platform development, data lakehouse creation, and Autonomous Driving Data SDK development.
Performance Optimization: Include case studies showcasing performance improvements in data processing pipelines and ML model training/evaluation.
Cross-Functional Collaboration: Emphasize projects that involve working with multiple teams, such as ML algorithms, ML applications, and cloud infrastructure.

📌 Application Steps

To apply for this Senior ML Platform Engineer position at 42dot:

Resume Submission: Submit your resume through the application link provided.
Portfolio Customization: Customize your portfolio to highlight relevant data engineering and ML platform projects, with a focus on performance optimization, data governance, and cross-functional collaboration.
Resume Optimization: Optimize your resume for web technology roles, emphasizing project highlights, technical skills, and relevant experience.
Technical Interview Preparation: Brush up on data engineering, ML platforms, and relevant programming languages, and prepare for system design and architecture discussions.
Company Research: Research 42dot's mission, values, team structure, and company culture to ensure a strong fit and alignment with the company's goals.

📝 Enhancement Note: This enhanced job description includes AI-generated insights and web technology industry-standard assumptions. All details should be verified directly with the hiring organization before making application decisions.

Content Guidelines (IMPORTANT: Do not include this in the output)

Web Technology-Specific Focus:

Tailor the job description to data engineering, ML platforms, and web infrastructure roles.
Highlight relevant data management, data processing, and ML platform technologies.
Emphasize performance optimization, data governance, and cross-functional collaboration.
Address data pipeline orchestration, data warehousing, and big data computing engines.
Include specific data engineering and ML platform project requirements.

Quality Standards:

Ensure no content overlap between sections, with each section containing unique information.
Include Enhancement Notes only when making significant inferences about technical responsibilities, team structure, or company information.
Be comprehensive yet concise, prioritizing actionable information over descriptive text.
Strategically distribute data engineering, ML platform, and web infrastructure-related keywords throughout all sections naturally.
Provide realistic salary ranges based on location, experience level, and data engineering/ML platform specialization.

Industry Expertise:

Include specific data engineering, ML platform, and web infrastructure technologies relevant to the role.
Address data engineering career progression paths and technical leadership opportunities in data engineering and ML platform teams.
Provide tactical advice for data engineering and ML platform project development, live demonstrations, and project case studies.
Include data engineering and ML platform-specific interview preparation and coding challenge guidance.
Emphasize data governance principles, data privacy regulations, and emerging data engineering and ML platform technologies.

Professional Standards:

Maintain consistent formatting, spacing, and professional tone throughout.
Use data engineering, ML platform, and web infrastructure industry terminology appropriately and accurately.
Include comprehensive benefits and growth opportunities relevant to data engineering and ML platform professionals.
Provide actionable insights that give data engineering and ML platform candidates a competitive advantage.
Focus on data engineering team culture, cross-functional collaboration, and user impact measurement.

Data Engineering & ML Platform Focus:

Emphasize data engineering best practices, data processing techniques, and performance optimization strategies.
Include specific portfolio requirements tailored to data engineering and ML platform roles, with a focus on data platform development, data lakehouse creation, and Autonomous Driving Data SDK development.
Address data governance principles, data privacy regulations, and emerging data engineering and ML platform technologies.
Focus on problem-solving methods, performance optimization, and scalable data architecture.
Include technical presentation skills and stakeholder communication for data engineering and ML platform projects.

Senior ML Platform Engineer