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# Software Engineer

- **Company:** Applied Intuition
- **Location:** United States (Remote available)
- **Experience:** Experienced
- **Contract:** Permanent contract
- **Skills:** Airflow, Big Data, Data Infrastructure, Distributed Systems, Python (Programming Language), Machine Learning, Software Engineering, Apache Spark, Information Technology, Data Analytics, Integration Frameworks, Apache Kafka, Machine Learning Operations, Data Pipelines
- **Published:** September 17, 2026
- **Apply:** https://startup.jobs/software-engineer-sds-core-evaluation-appliedintuition-com-8294802

## About the Role

* Master's, PhD, or equivalent work experience in Engineering such as Computer Science, Electrical Engineering, Software Engineering
 * 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
 * Ability to move fast, learn quickly, and mentor others while growing with the team

Nice to have:

 * Experience with automotive (AV) or robotics systems
 * Experience with highly automated ML training workflows
 * Prior contributions to systems that connect data-driven model iteration loops ("data flywheel")

## Description

SDS Evaluation owns how Applied Intuition measures the performance of its autonomy stacks. You'll build the datasets, metrics, and visualizations that tell perception, prediction, and planning developers whether a change improved the stack, where it remains weak, and what to work on next - the signal teams use to steer development and that leadership uses to judge release readiness.

This role sits at the intersection of autonomy engineering and measurement. You'll work closely with stack developers to turn their open questions into rigorous metrics, drawing on both real road data and novel synthetic scenario generation to evaluate behaviors too rare or too dangerous to encounter on the road. You'll also improve the evaluation infrastructure itself, making runs fast, reliable, and easy to interpret.

At Applied Intuition, you will:

 * Define and own metrics for one or more autonomy behavior areas, from requirements through implementation
 * Build and curate the datasets - real and synthetic - that those metrics run against
 * Design visualizations and reporting that make progressions and regressions obvious at a glance
 * Partner with perception, prediction, and planning teams to turn their open questions into measurable signal
 * Improve evaluation infrastructure: trigger paths, run time, reliability, and result presentation
 * Mentor junior engineers and contribute to defining best practices for data-centric development


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