Applied Intuition
Robotics · Autonomous-vehicle software
Robotics · Autonomous-vehicle software
Applied Intuition currently has 63 active AI-related roles in our index. The most common open titles are: Applied Perception Engineering Lead (2), Autonomy Integration Software Engineer, C++ Software Engineer (Autonomous Systems), C++ Software Engineer - Autonomy/Physical AI, Data & ML Pipeline Software Engineer. Most positions are in Engineering and Research.
Applied Intuition's active AI hiring is concentrated in: agents (32%), data (19%), serving infrastructure (14%). 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 (58 roles), South Korea (2 roles), United Kingdom (1 role).
Job postings at Applied Intuition most frequently reference: model serving, multimodal, inference infra, agent orchestration, vision.
In the past 30 days, Applied Intuition has posted 10 new AI-related roles. That is a +25% change versus the prior 30 days (8 → 10).
| 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 |
| 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 |
| Software Engineer - Performance Optimization Software Engineer focused on optimizing application-layer software for embedded systems in autonomous driving. The role involves analyzing and optimizing compute usage, profiling performance on embedded targets, and collaborating with ML runtime optimization engineers to ensure efficient model inference execution within tight compute budgets and real-world operating conditions. | Serve | 5 |