# Perception Engineer, Autonomy (Air Domain) at Applied Intuition

Applied Intuition is hiring a Perception Engineer, Autonomy (Air Domain). It pays $150k-$185k a year and Level rates it Builds AI ●●●●; you can [apply on Applied Intuition](https://jobsbylevel.com/go/e9a03a4b-4fc8-4df9-8d1b-6a5d93489dd0?utm_medium=ai-agent).

AI Level 4, AI centrality 90 out of 100. Fort Walton Beach, FL.

## Details

- Company: [Applied Intuition](https://jobsbylevel.com/companies/applied-intuition)
- AI level: AI Level 4 (score 90 out of 100)
- Location: Fort Walton Beach, FL
- Salary: $150k-$185k
- Posted: October 28, 2026
- Apply: https://jobsbylevel.com/go/e9a03a4b-4fc8-4df9-8d1b-6a5d93489dd0?utm_medium=ai-agent
- Free CV review for this job: https://jobsbylevel.com/cv-review?job=perception-engineer-autonomy-air-domain-at-applied-intuition-a78c69

## Prepare for this job

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## Description

Applied Intuition, Inc. is powering the future of physical AI. Founded in 2017 and now valued at $15 billion, the Silicon Valley company is creating the digital infrastructure needed to bring intelligence to every moving machine on the planet. Applied Intuition services the automotive, defense, trucking, construction, mining and agriculture industries in three core areas: tools and infrastructure, operating systems, and autonomy. Eighteen of the top 20 global automakers, as well as the United States military and its allies, trust the company’s solutions to deliver physical intelligence. Applied Intuition is headquartered in Sunnyvale, California, with offices in Washington, D.C.; San Diego; Ft. Walton Beach, Florida; Ann Arbor, Michigan; London; Stuttgart; Munich; Stockholm; Bangalore; Seoul; and Tokyo. Learn more at applied.co . We are an in-office company, and our expectation is that full-time employees primarily work from their Applied Intuition office 5 days a week. However, we also recognize the importance of flexibility and trust our employees to manage their schedules responsibly. This may include occasional remote work, starting the day with morning meetings from home before heading to the office, or leaving earlier when needed to accommodate family commitments. This in-office expectation does not apply to contractor positions About the role As a Perception Engineer, you will help build the perception layer of an autonomy stack for uncrewed air vehicles. The team turns noisy, intermittent data from many sensors and teammates into a reliable picture of the airspace. This role spans classical state estimation and tracking, sensor fusion, and learning-based prediction. Increasingly, those models adapt online during an engagement from limited and delayed feedback. The engineer will design and implement real-time C++ algorithms, build the tooling and simulation studies that show whether they work, and carry ideas from research prototypes into deployable software. We need someone fluent in both estimation theory and machine learning, rigorous about measuring results, and comfortable working at the line between research and production. At Applied Intuition, you will: Develop, integrate, and adapt cutting-edge AI/ML algorithms running on the perception autonomy stack to collect relevant information across a suite of platform sensors Create interfacing software to autonomously control sensors (e.g., EO, IR, acoustics, radar, and RF) Collaborate across the sensor, tracking, and autonomy teams to ensure seamless deployment of heterogeneous platform swarms in on-site DoD testing and demonstration events Deploy containerized solutions to embedded Linux devices, leveraging computer-in-the-loop testing and profiling, and efficiently collecting performance data Interact with the DoD customer to understand their use cases, requirements, and triage needs during field events to deliver a superior customer experience We're looking for someone who has: BS/MS in Electrical, Aerospace or Mechanical Engineering, Computer Science, Robotics, or a related field, plus 3+ years building perception or estimation software for autonomous systems. Strong modern C++ (17/20) for real-time, performance-critical code on Linux. Comfortable with linear algebra libraries (e.g. Eigen) and profiling. Solid grounding in state estimation: Kalman filter variants (EKF/UKF), multiple-model filters (IMM), and covariance and uncertainty handling. Experience with multi-target tracking and sensor fusion: data association, track management, and fusing heterogeneous sensors that report intermittently and with noise. Working knowledge of machine learning for prediction, including sequence models (RNN/LSTM, transformers), training pipelines, and evaluating models on held-out data. Python for data pipelines, analysis and visualization of logged and simulated runs. Experience validating algorithms in simulation and with recorded data: designing experiments, defining

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