The Role
Develop and deploy real-time classical perception algorithms for Aeva’s 4D FMCW LiDAR autonomous driving stack. Responsibilities include prototyping and optimizing algorithms, working with object detection, clustering, filtering, occupancy grids, data association, tracking, sensor fusion, and 3D data, while maintaining coding standards, documentation, tests, scalability, and production quality.
Summary Generated by Built In
About us:
Aeva’s mission is to bring the next wave of perception to a broad range of applications from automated driving to industrial robotics, consumer electronics, consumer health, security, and beyond. Aeva is transforming autonomy with its groundbreaking sensing and perception technology that integrates all key LiDAR components onto a silicon photonics chip in a compact module. Aeva 4D LiDAR sensors uniquely detect instant velocity in addition to 3D position, allowing autonomous devices like vehicles and robots to make more intelligent and safe decisions.
We are looking for a Senior Perception Engineer to work on our classical perception algorithms stack. You will be part of a growing team focused on exploring how far Aeva’s 4D FMCW LiDARs can push autonomous driving performance.
Responsibilities
- Contribute to Aeva’s 4D perception stack that will be deployed in production
- Ability to quickly prototype novel algorithms, implement them and iterate fast
- Ensure code is optimized for performance and scalability
- Adhere to coding standards and best practices to ensure consistency and quality in all code
- Take ownership of assigned tasks and ensure timely and accurate completion with documentation and tests
What you have
- 3+ years of experience with demonstrated ability to create and deploy real-time perception systems
- Have a MS or PhD in a relevant program such as Robotics, Computer Science, Electrical Engineering, or similar
- Experience with classical techniques such as object detection, clustering, filtering, occupancy grids, data association, and tracking algorithms
- Experience with techniques such as Kalman Filtering, Bayesian Methods, and Sensor Fusion (with LiDAR/IMU/GNSS/Camera)
- Experience with 3D data and representations (point clouds, meshes, trees, etc.)
- Strong fundamentals of linear algebra and 3D geometry
- Highly skilled in C++ development and experience with the Linux environment
- Skilled in scripting languages such as Python and Bash
Nice-to-have
- Knowledge of working principles for RADARs or LiDARs
- Experience with robotics or autonomous vehicle software frameworks such as ROS/ROS2
- Experience with run-time optimization techniques (e.g. parallelization, vectorization, cache-friendly development)
- Experience with modern build systems such as Bazel or similar
Skills Required
- 3+ years of experience creating and deploying real-time perception systems
- Master’s or PhD in Robotics, Computer Science, Electrical Engineering, or a related field
- Experience with object detection, clustering, filtering, occupancy grids, data association, and tracking algorithms
- Experience with Kalman filtering, Bayesian methods, and sensor fusion using LiDAR, IMU, GNSS, and cameras
- Experience with 3D data and representations such as point clouds, meshes, and trees
- Strong fundamentals in linear algebra and 3D geometry
- Advanced C++ development skills and Linux experience
- Scripting experience with Python and Bash
- Knowledge of RADAR or LiDAR operating principles
- Experience with robotics or autonomous vehicle frameworks such as ROS or ROS2
- Experience with runtime optimization, parallelization, vectorization, or cache-friendly development
- Experience with modern build systems such as Bazel
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The Company
What We Do
Aeva develops a new sensing & perception paradigm for autonomous machines. Aeva's technology brings together the best of vision, depth & motion sensors into a single product with superior performance. Compared to today's best in class, it provides greater range & resolution across weather conditions and a new dimension to precisely measure velocity of every pixel in the scene.
