Software Engineer II, Data Infrastructure - AV Labs
Role Overview
Uber is launching AV Labs to accelerate the autonomous technology ecosystem. We're building out a high-velocity team of multi-disciplinary experts to turn real-world operations into high-quality data for our autonomous partners. This team is focused on the hardest problem in AV today: unlocking real-world, long-tail driving data. Autonomy is now a data race—and Uber has an edge: We collect rare, real-world driving data at a scale and capital efficiency no one else can match.
We are seeking a Software Engineer II to build the data infrastructure for AV Labs: the systems that ingest, store, and index petabyte-scale multimodal sensor data (camera, lidar, radar, IMU, GNSS, and vehicle telemetry) from the fleet.
You will work closely with senior engineers to design and operate this infrastructure end to end. You will write clean, production-grade code, own features from design through deployment, and help keep the platform reliable and efficient as data volume grows by orders of magnitude.
What You'll Do
Build high-throughput pipelines that turn raw vehicle recordings into query-ready datasets, and make them correct under retries, reprocessing, and backfills.
Design and tune how data is laid out, versioned, and indexed on object storage so reads stay fast as the lake grows.
Define and evolve schemas and data contracts for heterogeneous sensor and telemetry data.
Run large batch workloads on distributed compute, and improve their throughput, cost, and failure behavior.
Own the operational health of what you build: metrics, alerting, on-call, and well-tested code.
Work with the teams that produce and consume the data to resolve quality issues and roll out changes safely.
Basic Qualifications
Bachelor's degree in Computer Science or a related technical field.
3+ years of professional software engineering experience building data infrastructure or distributed backend systems.
Proficiency in Python (or C++/Go) with strong fundamentals in data structures and design.
Hands-on experience with a distributed processing framework (e.g., Ray, Spark, or Flink).
Hands-on experience with columnar, object-storage-backed data formats and table formats (e.g., Parquet, Apache Arrow, Iceberg, or Delta).
Experience with observability tooling for batch systems (Prometheus, Grafana).
Preferred Qualifications
Master's degree in Computer Science or a related field.
Experience tuning storage layouts, compaction, or indexing for read-path performance.
Experience with robotics or sensor data (ROS 2, MCAP, point clouds, or time-series telemetry).
Experience running workloads on Kubernetes or a major cloud (OCI, AWS, or GCP).
Key Skills for This Role
Full Job Posting
About the Role
Uber is launching AV Labs to accelerate the autonomous technology ecosystem. We're building out a high-velocity team of multi-disciplinary experts to turn real-world operations into high-quality data for our autonomous partners. This team is focused on the hardest problem in AV today: unlocking real-world, long-tail driving data. Autonomy is now a data race—and Uber has an edge: We collect rare, real-world driving data at a scale and capital efficiency no one else can match.
We are seeking a Software Engineer II to build the data infrastructure for AV Labs: the systems that ingest, store, and index petabyte-scale multimodal sensor data (camera, lidar, radar, IMU, GNSS, and vehicle telemetry) from the fleet.
You will work closely with senior engineers to design and operate this infrastructure end to end. You will write clean, production-grade code, own features from design through deployment, and help keep the platform reliable and efficient as data volume grows by orders of magnitude.
What You'll Do
Build high-throughput pipelines that turn raw vehicle recordings into query-ready datasets, and make them correct under retries, reprocessing, and backfills.
Design and tune how data is laid out, versioned, and indexed on object storage so reads stay fast as the lake grows.
Define and evolve schemas and data contracts for heterogeneous sensor and telemetry data.
Run large batch workloads on distributed compute, and improve their throughput, cost, and failure behavior.
Own the operational health of what you build: metrics, alerting, on-call, and well-tested code.
Work with the teams that produce and consume the data to resolve quality issues and roll out changes safely.
Basic Qualifications
Bachelor's degree in Computer Science or a related technical field.
3+ years of professional software engineering experience building data infrastructure or distributed backend systems.
Proficiency in Python (or C++/Go) with strong fundamentals in data structures and design.
Hands-on experience with a distributed processing framework (e.g., Ray, Spark, or Flink).
Hands-on experience with columnar, object-storage-backed data formats and table formats (e.g., Parquet, Apache Arrow, Iceberg, or Delta).
Experience with observability tooling for batch systems (Prometheus, Grafana).
Preferred Qualifications
Master's degree in Computer Science or a related field.
Experience tuning storage layouts, compaction, or indexing for read-path performance.
Experience with robotics or sensor data (ROS 2, MCAP, point clouds, or time-series telemetry).
Experience running workloads on Kubernetes or a major cloud (OCI, AWS, or GCP).
Responsibilities
- For Sunnyvale, CA-based roles: The base salary range for this role is USD $171,000 per year - USD $190,000 per year.
- You will be eligible to participate in Uber's bonus program, and may be offered an equity award & other types of comp. All full-time employees are eligible to participate in a 401(k) plan. You will also be eligible for various benefits.
Qualifications
- Bachelor's degree in Computer Science or a related technical field.
- 3+ years of professional software engineering experience building data infrastructure or distributed backend systems.
- Proficiency in Python (or C++/Go) with strong fundamentals in data structures and design.
- Hands-on experience with a distributed processing framework (e.g., Ray, Spark, or Flink).
- Hands-on experience with columnar, object-storage-backed data formats and table formats (e.g., Parquet, Apache Arrow, Iceberg, or Delta).
- Experience with observability tooling for batch systems (Prometheus, Grafana).
Preferred Qualifications
- Master's degree in Computer Science or a related field.
- Experience tuning storage layouts, compaction, or indexing for read-path performance.
- Experience with robotics or sensor data (ROS 2, MCAP, point clouds, or time-series telemetry).
- Experience running workloads on Kubernetes or a major cloud (OCI, AWS, or GCP).
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