Experience Not stated in the posting - worth applying if the stack matches
About the role
structured by ORIGet in Touch For investor inquiries and device sales, please fill out the form below or email us directly at team@efference.ai. Name Email Message Send Message Open Roles We are hiring across hardware, perception, robotics, and infrastructure.
What you will do
- Develop and deploy robust VIO/VSLAM and 3D perception algorithms to run directly on-device.
- Optimize vision pipelines to maximize inference speed and efficiently leverage embedded compute resources.
- Collaborate with infrastructure and hardware teams to minimize end-to-end latency across the entire perception stack.
- Test, benchmark, and validate perception systems on physical lab robots in real-world environments.
- Diagnose and aggressively resolve tracking drops, algorithmic failure modes, and edge cases to ensure highly reliable spatial awareness.
What they are looking for
- Experience with 3D computer vision, Visual Inertial Odometry (VIO), or VSLAM systems.
- System-level understanding of perception pipelines, from raw sensor input to real-time spatial mapping.
- A strong focus on delivery, with a track record of deploying robust algorithms to edge environments.
- Strong C/C++ and Python programming skills.
- Familiarity with deploying and accelerating machine learning models on edge devices and dedicated Neural Processing Units (NPUs).
Nice to have
- Experience with model distillation, quantization, and edge AI optimization techniques.
- Familiarity with modern Vision Foundation Models and their deployment.
- Background working with stereo depth estimation or multi-sensor fusion (e.g., tightly coupled Camera + IMU systems).
3D computer visionVisual Inertial Odometry (VIO)VSLAMC/C++PythonMachine LearningModel DistillationQuantizationNPU
Full posting text
Experience: Experienced; requires 3D computer vision, VIO or VSLAM, C/C++, Python, and edge-device or NPU model deployment.
Develop and optimize on-device VIO/VSLAM and 3D perception systems for real-time robotic vision.