Software Engineer
Applied Intuition is a physical-AI company that provides software platforms for developing, validating, deploying, and operating intelligent vehicles and machines.
About Applied Intuition
Founded in 2017, Applied Intuition builds physical-AI tooling and infrastructure, a Vehicle OS, and a Self-Driving System for automotive, defense, trucking, construction, mining, agriculture, and robotics applications.
Skills
Candidate Availability
Required and preferred rules are kept separate and reflect the wording in the original posting.
About the Role
You will design, implement, and deploy software and machine learning components for behavior prediction and environmental interactions. You will build scalable inference, decision-making, and data-processing systems; transform logs into training datasets; evaluate model performance; refine estimation and planning algorithms; and maintain production-quality tools, libraries, testing, visualization, and workflow automation.
Requirements
- Bachelor's degree or foreign equivalent in Computer Science or a related field
- One year of relevant experience
- Python
- Go or C++
- Scalable software systems and data-intensive pipelines
- Deep learning frameworks
- Data modeling
- SQL
- Git
- Deep neural networks, generative models, or reinforcement learning
- Cloud infrastructure
- Containerization
- Software-service orchestration tools
Responsibilities
- Design, implement, and deploy software and machine learning components for behavior prediction and environmental interactions
- Build scalable software for inference and decision-making in dynamic environments
- Build and optimize pipelines for time-series and sensor data
- Transform raw logs into structured datasets for modeling and forecasting
- Develop testing and evaluation frameworks in simulated environments
- Measure model accuracy and robustness against ground-truth data
- Implement and refine state-estimation and decision-planning algorithms
- Apply generative and deep learning methods to uncertainty modeling
- Integrate models into low-latency production systems
- Develop tools and infrastructure for visualization, experiment tracking, and continuous model delivery
- Maintain modular code and rigorous testing strategies
Benefits
- Equity