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Rivian

Makes electric pickups, SUVs and delivery vans

Staff Software Engineer - ML Data Infrastructure, Autonomy

Backend EngineeringUpdated on Nov 12
Full-timePalo Alto, CA, USA$206.5k - $258.1kIn-person

About the Job

Requirements

  • A bachelor's degree in Computer Science, Electrical Engineering, or a related field, or equivalent experience.
  • At least 8 years of software engineering experience, with a strong focus on data infrastructure or data platform work.
  • At least 5 years owning a large-scale data platform for ML or analytics workloads, including its storage and access model.
  • At least 5 years with columnar and analytical stores such as ClickHouse, Pinot, Druid, BigQuery, Databricks, or Snowflake, including high-cardinality data modeling and query performance tuning.
  • At least 3 years designing storage and query layers for large volumes of heterogeneous metrics, such as KPIs or performance telemetry.
  • At least 3 years with document or NoSQL stores, including schema design, indexing strategy, sharding, and operation at scale.
  • At least 3 years with modern data and table formats such as Parquet, Arrow, Iceberg, or Delta.
  • At least 3 years building batch and streaming pipelines at scale with production orchestration and data quality gates.
  • At least 3 years of hands-on Python experience, plus production experience in Go, C++, or Rust for performance-sensitive parts of an access layer.
  • At least 3 years with queueing and event-driven systems such as SQS, Kafka, or Kinesis in a production ingest path.
  • At least 2 years with production monitoring and alerting for data pipelines using Prometheus, Grafana, Datadog, or CloudWatch, including automated integrity and freshness checks.
  • At least 2 years migrating a production data platform between storage or format architectures incrementally while it remains in service.
  • Experience setting technical vision, timelines, and priorities for a project or team, acting as a technical lead, and mentoring engineers.
  • Ability to turn ambiguous, high-level requirements into a detailed system design and drive it to completion independently.
  • Willingness to work through implementation details and a record of raising technical standards across a broader engineering organization.
  • A record of designing schemas and interfaces that other teams build on and that withstand changing requirements.

Responsibilities

  • Own the unified autonomy data layer, providing a single interface over document metadata, high-cardinality columnar analytics, real-time search, and object-stored sensor payloads so engineers can query concepts rather than databases.
  • Design the data lake architecture, metadata access, and APIs that provide engineers with a single access layer.
  • Own the data access and format strategy, including the current canonical production log format and future ML-native columnar and vector access; define canonical schemas, manage their evolution, and drive the migration path.
  • Design and build batch and streaming pipelines for evaluation data and their storage layer to handle the volume and diversity of metrics from on-road and simulation runs at their actual cardinality.
  • Build the indexing and discovery layer for fast semantic and metadata search across fleet data, including scenario tagging, event indexing, embedding-based similarity search, and the query surface engineers use.
  • Improve data mining tools that apply ML techniques to data discovery so Perception, Behavior, and Planning engineers can find rare and long-tail scenarios at fleet scale.
  • Build well-documented tools and APIs so engineers outside data infrastructure can find, slice, and materialize the data they need without writing a pipeline.
  • Establish data correctness practices, including schema validation, producer-consumer contracts, freshness and completeness monitoring, and alerting that catches bad data before model training.
  • Build continuous platform testing and monitoring covering ingest health, data freshness, schema conformance, and query performance.
  • Design for the workflows of Perception, Behavior, Planning, and Simulation engineers.
  • Work with the security and privacy team on retention, access control, consent handling, and regional data requirements for vehicle-collected data.
  • Set data engineering standards across Autonomy and mentor engineers on schema design, query performance, and pipeline reliability.

Desired Qualifications

  • Applied ML for data problems, such as data mining, active learning, or embedding-based retrieval for rare and long-tail scenarios.
  • Robotics or autonomous vehicle data experience, including ROS or ROS 2, MCAP, rosbag, and time synchronization across sensor modalities.
  • Experience with vector databases and embedding-based retrieval for data mining and scenario discovery.
  • Experience with ML training data pipeline performance, including dataloader throughput, sharding, and shuffling.
  • Experience with feature stores, data catalogs, or lineage systems.

About the Company

Rivian

Makes electric pickups, SUVs and delivery vans

Rivian designs and builds electric pickup trucks, SUVs, and delivery vans, pairing off-road ability with electronics and software it develops itself. In 2021, its R1T became the first fully electric pickup truck on the US consumer market, ahead of larger rivals such as Ford and Tesla. It makes the R1T pickup, the R1S and R2 SUVs, and commercial vans in Normal, Illinois, and sells them directly online and in its own stores rather than through dealers. Its vehicles run on Rivian's own software, which gains features through updates sent over the internet, and most revenue comes from vehicle sales, with service and optional software adding more. Rivian was founded in 2009 and is based in Irvine, California.

Company size
10,001+
Company stage
IPO
Headquarters
Irvine, CA
Founded
2009

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