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Senior ML Platform Engineer (Autonomous Driving)

Sunnyvale, United States · San Francisco, Califonia, US

Pay
Pay amount needs review — pay source
Compensation $133,000 - $254,0000 Additional Information
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Work setting
Hybrid — work setting source
Location type: Hybrid
From the employer’s posting
Employment
Full-time — employment source
Employment type: Full-time
From the employer’s posting
Team
ENGINEERING › Autonomous Driving — team source
Department: ENGINEERING › Autonomous Driving
From the employer’s posting

Before you apply

Sponsorship
Visa sponsorship not confirmed — sponsorship source
2. Do you now, or will you in the future, require immigration sponsorship for work authorization (for example, H-1B status)?
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Apply at 42dot

What you’ll work on

Full posting
  • Set technical strategy and oversee a high-scale data platform for ML training and validation

  • Build a data lakehouse for autonomous driving scene, sensor, calibration and annotation data

  • Drive development of the Autonomous Driving Data SDK, including scene search and dataset loading

  • Find and fix performance bottlenecks across data processing and search pipelines

  • Bootstrap and maintain data pipeline, database, lakehouse and serving infrastructure

From the employer’s posting
Set technical strategy and oversee development of high scale, reliable data platform
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
Bootstrap and maintain infrastructure for Data Platform components—Data Processing Pipeline, Database, Data Lakehouse and Data Serving.

What you’ll bring

All qualifications

Core experience

  • Bachelor's degree or higher in Computer Science, Engineering, Robotics, or a similar technical field.
  • 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
  • Experience with Apache Spark or other big data computing engines
  • Excellent leadership and communication skills, with a demonstrated ability to lead technical projects

Preferred experience

  • 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
Qualification wording
Bachelor's degree or higher in Computer Science, Engineering, Robotics, or a similar technical field.
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
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
Education & alternatives
Qualifications - 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

Tools in this posting

  • Python
  • Databricks
  • Delta Lake
  • MongoDB
  • PostgreSQL
  • Spark
  • Airflow
  • PyTorch
  • TensorFlow
Source — Tool mentions in context
- 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)
- 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
- 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
- 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
- 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

About 42dot

42dot is a mobility AI company committed to solving mobility challenges with software and AI.

In the employer’s words · Read in context · Company website ↗

Posting record

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Open 4 monthsWe first saw this posting 149 days ago and it is still up. Roles open this long are often slow to fill or kept open to collect applications.

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Last seen by us
Oct 29, 2026
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Apr 29, 2026

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Job description

View original posting ↗

We are looking for the best
 

About Us

42dot is a mobility AI company committed to solving mobility challenges with software and AI. As the Global Software Center of Hyundai Motor Group, 42dot pioneers the future of mobility by advancing the development of software-defined vehicles.

We develop safety-first, user-centric software-defined vehicle technologies that deliver the latest performance through continuous updates like smartphones. By advancing software and AI technology, 42dot envisions a world where everything is connected and moves autonomously through a self-managing urban transportation operating system.

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.

 

Responsibilities

  • 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.

 

Qualifications

  • 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

 
 

Preferred Qualifications

  • 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

 

Interview Process

  • Application Review - Coding Test - 1st interview - 2nd interview - Offer Negotiation - Hiring

  • The screening procedures may vary depending on the position, schedule, or other circumstances.

    You will be individually notified of the screening schedule and results via the email address provided in your application.

 

Compensation

  • $133,000 - $254,0000

 

Additional Information

  • In accordance with fair hiring practices, do not include any personal information unrelated to your job qualifications (e.g., Social Security Number, family relations, marital status, age, photo, physical condition, place of birth, etc.) in your resume.

  • All documents must be submitted in PDF format and under 30MB in size.

  • If you experience issues uploading your resume, please send it along with the job posting URL to recruit@42dot.ai.

  • We strongly encourage applications from U.S. veterans and candidates eligible for employment preference under applicable laws.

  • Qualified individuals with disabilities are encouraged to apply and will receive consideration under the Americans with Disabilities Act (ADA).

  • 42dot does not accept unsolicited resumes and will not pay fees for any such submissions. Equal Opportunity Statement

  • 42dot is an Equal Opportunity Employer. We celebrate diversity and are committed to creating an inclusive environment for all employees, regardless of race, color, religion, sex, sexual orientation, gender identity, national origin, age, disability, or veteran status.

 

※ Please review the following information before applying.

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Pay
Compensation $133,000 - $254,0000 Additional Information
Location & working pattern

San Francisco, Califonia, US

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