Data & ML Pipeline Software Engineer

Applied Intuition•Sunnyvale, CA

6h•Onsite

About The Position

The Data and Test Flywheel Engineer will be a key member of Applied Intuition’s data flywheel initiative — building the systems that connect vehicle data collection, training, and automated model improvement. You’ll create the infrastructure that allows our autonomous driving stack to continuously learn from real-world and simulation data, accelerating development across teams working on perception, planning, and control. This role sits at the intersection of large-scale data engineering and machine learning infrastructure. You’ll work closely with ML engineers and system developers to automate data selection, curation, and model iteration so our vehicles can self-improve with minimal human intervention.

Requirements

3–5 years of experience in software or data infrastructure engineering.
Expertise in building and scaling data pipelines, distributed systems, or ML infrastructure.
Proficiency in Python and strong knowledge of data frameworks (Spark, Airflow, Kafka, etc.).
Experience working with large-scale datasets and understanding data-driven development cycles.
Familiarity with machine learning workflows or model training/deployment, especially automation of those processes.
Strong systems thinking and ability to work across multiple parts of the stack (data, infra, and ML).
Interest in seeing the direct impact of your infrastructure work on how vehicles perform and improve.

Nice To Haves

Experience with automotive (AV) or robotics systems.
Previous work on ML platforms for large-scale products (e.g., Ads, Recommendation, or Autonomy pipelines).
Experience with highly automated ML training workflows.
Prior contributions to systems that connect data-driven model iteration loops (“data flywheel”).
Ability to move fast, learn quickly, and mentor others while growing with the team.

Responsibilities

Build and maintain large-scale data processing pipelines (ETL) for ingesting and curating driving datasets.
Design and implement systems that automate data selection, labeling, training, and testing loops.
Collaborate with modeling teams to improve training efficiency and model performance across iterations.
Develop the core infrastructure that closes the loop between real-world test results and new model deployments.
Use your engineering expertise to help Applied Intuition’s vehicles learn from data at scale, improving safety and performance.
Mentor junior engineers and contribute to defining best practices for data-centric development.