Applied Intuition has 5 active AI-related job listings. The majority of these roles, 80%, are focused on agents, with 20% in post-training. The company is hiring for engineering roles in the United States and Japan. Their technical focus appears to involve agent orchestration, multimodal capabilities, and vision systems. In the last 30 days, Applied Intuition posted 6 new AI roles, a 40% decrease compared to the previous 30-day period.
Currently tracking 14 active AI roles, up 45% versus the prior 4 weeks. Primary focus: Agent · Engineering. Salary range $100k–$423k (avg $212k).
Applied Intuition currently has 15 active AI-related roles in our index. The most common open titles are: Autonomy Integration Software Engineer (2), Perception & Fusion Engineer (2), Robotic Software Engineer, Perception (2), Senior C++ Software Engineer (Collaborative Autonomy) (2), LVC Simulation Integration Engineer. Most positions are in Engineering and Product.
Applied Intuition's active AI hiring is concentrated in: agents (80%), data (20%). These categories follow a seven-stage AI lifecycle: data, pre-training, post-training, serving infrastructure, agents, evaluation, and application.
Applied Intuition is hiring AI talent in: United States (10 roles), Japan (2 roles).
In the past 30 days, Applied Intuition has posted 19 new AI-related roles. That is a +217% change versus the prior 30 days (6 → 19).
| Title | Stage | AI score |
|---|---|---|
| Engineering Manager - ML Platform and Infrastructure Engineering Manager for ML Platform and Infrastructure at Applied Intuition, focusing on building and scaling the infrastructure for Physical AI. The role involves managing a team to own training & inference orchestration, GPU cluster architecture, and performance optimization for large-scale ML workloads. | ServeData | 8 |
| Head of Defense Engineering (South Korea) Head of Defense Engineering in Seoul, South Korea, responsible for leading a local team to deliver simulation and AI tooling for autonomous defense systems. The role involves setting technical direction, growing the team, ensuring reliable delivery of defense-grade products, and partnering with US leadership. Requires a strong technical background in autonomy, simulation, or AI/ML infrastructure, along with people management experience and excellent English and Korean communication skills. |
| Serve |
| 7 |
| Applied Perception Engineering Lead Lead a team developing Perception pipelines for defense applications, integrating pre-trained models into real-time systems deployed on hardware. Focus on EO/IR, Radar, and other sensor modalities, with responsibilities for perception autonomy behaviors. | ServePost-train | 7 |
| Applied Perception Engineering Lead Lead a team developing and deploying Perception pipelines for defense applications, integrating pre-trained models into real-time sensor systems (EO/IR, Radar) and managing perception autonomy behaviors. Focus on production-level software development and deployment to hardware. | Serve | 7 |
| Embedded AI Engineer – Android Automotive (On-Device Intelligence) This role focuses on deploying and optimizing on-device ML inference and learning systems for Android Automotive. It involves implementing multimodal LLMs, integrating models with specific runtimes, profiling for strict performance budgets, and designing safety guardrails for model outputs within embedded constraints. | ServeAgent | 7 |
| Sensor Validation Engineer This role focuses on validating and characterizing sensor performance for autonomous systems, using both ML and physical models within a simulation environment. The engineer will design tests, develop data collection tools, improve simulation models, and work with customers to validate their models. | ServeData | 7 |
| ML Runtime Optimization Engineer ML Runtime Optimization Engineer focused on optimizing ML model performance and inference on embedded runtime environments for physical AI applications in robotics and autonomous systems. | Serve | 7 |
| Senior Software Engineer - ML Infrastructure Senior Software Engineer focused on ML Infrastructure, building and integrating end-to-end ML pipelines, distributed cloud GPU training, and evaluation systems. The role spans the entire ML lifecycle, working with modeling teams to solve complex data problems at scale and contribute to a company-wide platform for ML training, evaluation, and deployment. | ServeEval Gate | 7 |