Applied Intuition
Software Engineer (SDS Core - Data and Test Flywheel)
About this role
Applied Intuition seeks a Data & ML Pipeline Software Engineer to build large-scale infrastructure that enables autonomous vehicles to continuously learn from real-world and simulated data. You'll design automated systems connecting data collection, model training, and deployment, working across data engineering, ML, and autonomous driving teams in Sunnyvale.
What you'll do
- Build and maintain large-scale ETL pipelines for ingesting and curating driving datasets
- Design automated systems for data selection, labeling, training, and testing loops
- Collaborate with ML teams to optimize training efficiency and model performance
- Develop infrastructure linking real-world test results to new model deployments
- Mentor junior engineers and establish best practices for data-centric development
- Enable vehicles to learn from data at scale to improve safety and performance
What they're looking for
- Python programming
- Data pipeline frameworks (Spark, Airflow, Kafka)
- Distributed systems design
- ML infrastructure and workflow automation
- Large-scale dataset handling
- Systems thinking and full-stack knowledge
- ML model training and deployment
- Data engineering architecture
Benefits
- Base salary, equity (options/RSUs), and comprehensive health/dental/vision insurance
- Life and disability insurance coverage
- 401k retirement with employer match
- Learning and wellness stipends
- Paid time off
- In-office work in Sunnyvale with flexible remote options
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Applied Intuition
Applied Intuition builds autonomous vehicle and defense systems software, including motion planning algorithms, simulation infrastructure, and autonomy integration platforms for aerial and ground platforms. The company is hiring for security engineers, robotics/autonomy software engineers, hardware-in-the-loop specialists, and IT operations professionals to support its growing physical AI operations.
- Website
- appliedintuition.com
Likely interview questions
- Walk us through a large-scale data pipeline you've built from scratch. What frameworks did you use, and how did you handle data quality and scale challenges?
- Describe your experience automating ML workflows or training loops. How did you approach closing feedback loops between model evaluation and retraining?