Applied Intuition
Robotics · Autonomous-vehicle software
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.
Robotics · Autonomous-vehicle software
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.
| Title | Stage | AI score |
|---|---|---|
| Robotic Software Engineer (Drone Stack) Robotic Software Engineer focused on designing, integrating, and deploying autonomy and vehicle intelligence systems for defense and commercial customers, particularly on unmanned aerial vehicles (UAVs). The role involves significant hardware-software integration, troubleshooting, and testing in simulated and live environments. | AgentServe | 7 |
| Robotic Software Engineer, Perception Robotic Software Engineer focused on developing, integrating, and maintaining real-time AI/ML sensor algorithms for autonomous vehicles. This role involves creating interfacing software for various sensors, collaborating with autonomy teams, deploying solutions to embedded systems, and interacting with customers for use case understanding and issue triage. | Post-train |
| 7 |
| Engineering Manager - Data Intelligence Engineering Manager for Data Intelligence team focused on producing, curating, and leveraging high-quality data for autonomy development. Responsibilities include managing engineers, prioritizing data quality systems, labeling workflows, and data mining, and integrating foundation models to enhance these processes. | DataPost-train | 7 |
| Software Engineer - Defense Tooling Software Engineer role focused on building simulation and AI tooling for autonomous defense systems. Responsibilities include designing and developing features, contributing to system architecture, mentoring junior engineers, and collaborating with US teams and customers. Requires experience with simulation platforms, ML training infrastructure, or developer tooling, and reinforcement learning frameworks. | Data | 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 |
| Senior Neural Rendering Software Engineer Senior Neural Rendering Software Engineer to own end-to-end development and optimization of neural rendering systems, including 3D Gaussian-based representations and other learned scene representations, across multiple products and sensor modalities. Design and scale GPU pipelines using CUDA for neural scene training, inference, and real-time rasterization. Architect and optimize multi-threaded C++ systems for scene management, streaming, scheduling, and CPU-side orchestration of neural and hybrid rendering workloads. Research, prototype, and productionize state-of-the-art neural rendering and reconstruction techniques. | Post-trainServe | 7 |