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42dot

Unified platform for multi-modal urban mobility

Senior ML Platform Engineer - Autonomous Driving

Applied Machine LearningConfirmed live in the last 24 hours
Full-time$133k - $254k /yrHybrid

About the Job

Requirements

  • A bachelor's degree or higher in Computer Science, Engineering, Robotics, or a similar technical field is required.
  • At least 7 years of experience in Data Engineering or ML Platform roles is required.
  • Expert-level proficiency in Python and solid experience with Python SDK development are required.
  • Solid working experience with databases such as MongoDB or PostgreSQL is required.
  • A strong understanding of modern AI frameworks such as PyTorch or TensorFlow, especially the principles of distributed data loaders for model training, is required.
  • Hands-on experience with data pipeline job orchestration using Databricks Workflows or Apache Airflow, and integrating data pipelines with machine learning models, is required.
  • Extensive experience with data technologies and architectures such as data warehouses (e.g., Hive) or lakehouses (e.g., Delta Lake) is required.
  • Experience with Apache Spark or other big data computing engines is required.
  • The candidate must demonstrate the ability to lead technical projects.

Responsibilities

  • Set technical strategy and oversee development of a high-scale, reliable data platform to manage, visualize, and serve large-scale datasets for machine learning model training and validation.
  • Build the data lakehouse for autonomous-driving scene datasets, including sensor, calibration, and annotation data.
  • Drive development of the Autonomous Driving Data SDK, including scene data search, dataset preparation, and dataset loading.
  • Investigate performance bottlenecks across data-processing pipelines, including data-processing latency, data-search latency, and Test Procedure (TP) coverage.
  • Bootstrap and maintain infrastructure for data-platform components, including the data-processing pipeline, database, data lakehouse, and data serving.
  • Collaborate with machine learning algorithm, machine learning application, and cloud infrastructure teams to align machine learning platforms with the overall autonomous-driving system architecture.

Desired Qualifications

  • Experience with autonomous-vehicle sensor data such as LiDAR, camera, or radar is preferred.
  • Experience with the machine learning model training lifecycle, including data preparation, model training, validation, and deployment, is preferred.
  • Understanding of data governance principles and data privacy regulations, and experience implementing security measures to protect data, are preferred.
  • Understanding of large models such as VLM is preferred.

About the Company

42dot

Unified platform for multi-modal urban mobility

42dot.ai provides a platform called UMOS (Urban Mobility Operating System) that connects and manages multiple transportation modes—such as cars and drones—within a single system for cities, agencies, and private clients. UMOS enables efficient transportation management, demand-response services, and smart logistics by integrating data and operations across modes, powering operations through a subscription-based platform-as-a-service model. Unlike single-mode solutions, 42dot.ai offers multi-modal, city-focused orchestration that bridges public, private, and logistics needs. The company aims to transform urban mobility by helping cities run more efficiently, cut costs, and improve the experience for commuters and logistics providers.

Company size
501-1,000
Company stage
Growth Equity (Venture Capital)
Total funding
$731.9M
Headquarters
Seoul, South Korea
Founded
2019

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