Avride needs to design, build, and maintain the core data and machine learning infrastructure to ingest, process, and organize petabytes of telemetry and sensor data into a globally distributed data lake, enabling high-throughput, low-latency access to data for both model training and online inference. This will help ML engineers and data scientists iterate faster and deliver better-performing systems.
Requirements
- Strong proficiency in Python (required); experience with C++ is highly desirable
- Proven ability to write high-quality, maintainable code and design scalable, robust systems
- Experience with Kubernetes for deploying and managing distributed systems
- Hands-on experience with large-scale open-source data infrastructure (e.g., Kafka, Flink, Cassandra, Redis)
- Deep understanding of distributed systems and big data platforms, with experience managing petabyte-scale datasets
- Experience building and operating large-scale ML systems
- Understanding of ML/AI workflows and experience with machine learning pipelines
Responsibilities
- Build and maintain robust data pipelines and core datasets to support simulation, analytics, and machine learning workflows, as well as business use cases
- Design and implement scalable database architectures to manage massive and complex datasets, optimizing for performance, cost, and usability
- Collaborate closely with internal teams such as Simulation, Perception, Prediction, and Planning to understand their data requirements and workflows
- Evaluate, integrate, and extend open-source tools (e.g., Apache Spark, Ray, Apache Beam, Argo Workflows) as well as internal systems
Other
- Candidates are required to be authorized to work in the U.S.
- The employer is not offering relocation sponsorship
- Remote work options are not available.
