42dot에서 자율주행을 위한 ML 플랫폼을 구축할 Senior ML Platform Engineer를 채용합니다. 데이터 엔지니어링 및 ML 플랫폼 분야에서 7년 이상의 경력이 필수입니다. Python 숙련도와 데이터 레이크하우스, 분산 처리 시스템에 대한 깊은 이해가 필요합니다. 대규모 데이터셋 관리 및 성능 최적화를 주도하며 기술 전략을 수립합니다.
At 42dot, our AD ML Platform Engineers build the core data platform and ML training / eval platform for the cutting edge algorithms in autonomous driving. We develop the distributed system of a scalable data platform for large-scale dataset (millions of scenes), as well as high-performance data serving SDKs for ML model training / evaluation. The platforms we deliver could highly improve the efficiency of ML model development lifecycle, including training, evaluation, deployment, as well as monitoring in the cloud environment.
Set technical strategy and oversee development of high scale, reliable data platform to manage, visualize and serve large-scale datasets for ML model training and validation.
Build up the data lakehouse for autonomous driving scene datasets, including the sensor data, calibration data, as well as annotation data
Drive the Autonomous Driving Data SDK development, including scene data search, datasets preparation, dataset loading, etc.
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.
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
Expert-level proficiency in Python and solid experience in Python SDK development
Solid working experience in Databases (e.g., MongoDB, PostgreSQL, etc)
Strong understanding of modern AI frameworks (e.g., PyTorch, TensorFlow etc.), especially the principle of distributed data loader for model training
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
Excellent leadership and communication skills, with a demonstrated ability to lead technical projects
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 data governance principles, data privacy regulations, and experience implementing security measures to protect data
Understanding of Large Models, like VLM
Application Screening
Coding Test
First Interview (Virtual, approximately 1 hour)
Second Interview (In-person or Virtual, approximately 3 hours)
Offer Discussion / Onboarding
Please do not include legally prohibited information in your application (e.g., ID number, family relations, marital status, salary, photo, physical details, hometown).
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.
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False information in your application may result in offer cancellation.
A reference check may be conducted after the interview process, with your consent.
