Data & ML Pipeline Software Engineer

Overview Applied Intuition, Inc. is powering the future of physical AI. The company designs the digital infrastructure to bring intelligence to moving machines and serves automotive, defense, trucking, construction, mining, and agriculture industries in three core areas: tools and infrastructure, operating systems, and autonomy. About The Role 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. 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. Qualifications Bachelor\'s or higher degree 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. Nice To Have 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. Compensation & Benefits Compensation at Applied Intuition for eligible roles includes base salary, equity, and benefits. Base salary is a single component of the total compensation package, which may also include equity in the form of options and/or restricted stock units, comprehensive health, dental, vision, life and disability insurance coverage, 401k retirement benefits with employer match, learning and wellness stipends, and paid time off. Note that benefits are subject to change and may vary based on jurisdiction of employment. The base salary range for this full-time position is $125,000 - $222,000 USD annually. Equal Opportunity Applied Intuition is an equal opportunity employer and federal contractor or subcontractor. The parties agree to abide by applicable laws, including 41 CFR 60-1.4(a), 41 CFR 60-300.5(a) and 41 CFR 60-741.5(a). These regulations prohibit discrimination and require affirmative action where applicable. The parties also adhere to Executive Order 13496 (29 CFR Part 471, Appendix A to Subpart A) regarding employee rights under federal labor laws. #J-18808-Ljbffr Applied Intuition