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Applied Intuition is a $15B-valued AI infrastructure company (founded 2017) powering physical AI for autonomous systems across automotive, defense, trucking, construction, mining, and agriculture. The company serves 18 of the top 20 global automakers and the US military, with a global presence spanning North America, Europe, and Asia.
In this Software Engineer role, you will design, implement, and deploy machine learning and software components focused on behavior prediction and environmental interactions for autonomous systems. Key responsibilities include:
• Design and build scalable software for inference and decision-making in dynamic, real-time environments
• Develop and optimize data pipelines to process complex datasets including time-series and sensor data from physical systems
• Transform raw logs into structured datasets for training large-scale modeling and forecasting algorithms
• Create testing and evaluation frameworks to validate system performance in simulated environments
• Measure model accuracy and robustness against real-world ground truth data to drive continuous improvement
• Implement algorithms for state estimation and decision planning
• Leverage deep learning and generative models to improve efficiency and accuracy in uncertainty handling
• Collaborate cross-functionally to integrate ML models into production systems with low-latency performance requirements
• Develop tools, libraries, and infrastructure for experiment tracking, visualization, and continuous model delivery
• Maintain high code quality through modular design, comprehensive testing (mocks, regression tests), and rigorous practices for data-intensive systems
You will work in Applied Intuition's Sunnyvale headquarters with an in-office expectation of 5 days/week, though the company supports occasional flexibility for remote work and schedule adjustments.
Required qualifications: Bachelor's degree in Computer Science or related field; 1+ year professional or academic experience with Python, Go or C++, scalable software systems, data pipelines, deep learning frameworks, data modeling, SQL, Git, neural networks/generative models/reinforcement learning, cloud infrastructure, containerization, and orchestration tools.
