Senior ML Platform Engineer (Autonomous Driving)
San Francisco, Califonia, US
- Pay
Pay amount needs review — pay source
Compensation $133,000 - $254,0000 Additional Information
Read the full posting- Work setup
- Unconfirmed
- Employment
- Unconfirmed
What you’ll work on
Full postingBuild 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.
Collaborate with cross-functional teams, including ML algorithm, ML application, and Cloud Infra to align ML Platforms with overall Autonomous Driving System Architecture.
From the employer’s posting
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.
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
What you’ll bring
All qualificationsCore 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
Tools in this posting
- Python
- Databricks
- Delta
- 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
Job description
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.
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Source & posting history
Source notes
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- Pay
Compensation $133,000 - $254,0000 Additional Information
- Location & working pattern
San Francisco, Califonia, US
Working pattern and location restrictions need checking in the full posting.
- Work authorization
No clear work-authorization passage found. Eligibility is unconfirmed.
- Status in our records
- Active
- First seen by us
- Jun 2, 2026
- Recorded sightings
- 144
- Last seen by us
- Oct 8, 2026
- Employer says posted
- Apr 29, 2026
These dates show when we found the listing. Check the employer’s website to confirm it is still accepting applications.
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