You Will..

- Design and implement data pipelines using real-world driving data and Waabi World (our high-fidelity simulator) to train and evaluate deep learning models.

- Optimize data formats, caching, and dataloading to drive highly efficient ML training and evaluation at scale.

- Improve data sampling and composition for deep data introspection to track model performance and uncover critical edge-case scenarios.

- Champion engineering excellence by writing high-quality, well-structured, and rigorously tested code.

- Help drive project roadmap planning, prioritization, and delivery.

Qualifications:

- BS or MS in Computer Science, Machine Learning, or a related technical field, with 4+ years of industry experience.

- Proficiency in Python and strong software engineering fundamentals, including experience with deep learning frameworks such as PyTorch, TensorFlow, or JAX.

- Hands-on experience building distributed ETL and data processing pipelines.

- Direct experience managing ML pipelines, including dataset management, dataloading, and optimization.

- Strong understanding of cloud job orchestration, monitoring, and instrumentation best practices.

- A collaborative, open-minded approach with a passion for tackling hard problems in autonomous technology and a strong willingness to mentor others.

Bonus Points:

- Experience with optimizing large scale distributed training pipelines and/or highly optimized ML inference pipelines.  

- Experience with MapReduce (Apache Hadoop/Spark) or orchestration frameworks (Apache Airflow, Apache Beam, Google Cloud Dataflow, AWS Step Functions).

- Experience solving data challenges specific to autonomous driving.

- Familiarity with linear algebra (projections, transforms) and 3D geometry.

- Experience working with multimodal sensor data (e.g., LiDAR, RADAR, camera).