[About the Team & Mission] 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

• 서류전형 - 코딩테스트 - 화상면접 (1시간 내외) - 대면 혹은 화상면접 (3시간 내외) - 최종합격

[Additional Information] • 전형 절차는 일정 및 진행 상황에 따라 일부 변경될 수 있으며, 각 전형 결과는 등록하신 이메일로 개별 안내드립니다. • 지원서 제출 시 주민등록번호, 가족관계, 혼인 여부, 연봉, 사진, 신체조건, 출신 지역 등 채용절차법상 요구 금지된 정보는 제외 부탁드립니다. • 지원서 접수 중 오류가 발생하거나 기타 문의 사항이 있을 경우, [email protected]로 문의해 주시기 바랍니다. • 국가보훈대상자 및 취업보호 대상자는 관계법령에 따라 우대합니다. • 장애인 고용 촉진 및 직업재활법에 따라 장애인 등록증 소지자를 우대합니다. • 42dot은 의뢰하지 않은 서치펌의 이력서를 받지 않으며, 요청하지 않은 이력서에 대해 수수료를 지불하지 않습니다. • 지원서 내용 중 허위 사실이 발견될 경우, 입사가 취소될 수 있습니다. • 인터뷰 프로세스 종료 후 지원자의 동의하에 평판조회가 진행될 수 있습니다. • 3개월의 수습기간이 적용될 수 있습니다.