We're looking for a seasoned Machine Learning Engineer who is passionate about building intelligent and immersive AR experiences. In this role, you'll architect and deliver advanced ML and computer vision solutions that bring real-world and digital environments together. You'll work closely with cross-functional teams-product managers, designers, and engineering teams-to transform innovative ideas into scalable, real-time AR applications.
If you enjoy solving complex technical challenges, pushing the boundaries of AR/VR/XR, and leading strategic ML initiatives, this role is for you.
-
Lead end-to-end development of machine learning models for AR-from research and prototyping to model training, optimization, and production deployment.
-
Build and optimize computer vision and deep learning models for object detection, tracking, segmentation, spatial mapping, and real-time scene understanding.
-
Develop real-time inference pipelines for mobile and edge devices (iOS, Android, AR glasses, and XR hardware).
-
Work with technologies such as 3D reconstruction, SLAM, scene mapping, gesture recognition, and sensor fusion.
-
Integrate ML models into AR platforms including ARCore, ARKit, Unity, Unreal Engine, or custom XR frameworks.
-
Continuously improve performance, latency, accuracy, and overall user experience for AR workloads.
-
Provide mentorship and technical leadership while driving ML best practices and architecture decisions.
-
Collaborate on roadmap planning and contribute to overall technology strategy.
-
12+ years of experience in Machine Learning, Deep Learning, and Computer Vision.
-
Strong expertise in building ML models for AR/VR/XR or other real-time interactive 3D applications.
-
Solid hands-on experience with CNNs, RNNs, Transformers, 3D CV models, Diffusion models, and Generative AI.
-
Advanced programming skills in Python and experience with frameworks such as TensorFlow, PyTorch, OpenCV, ONNX.
-
Experience in model optimization (quantization, pruning) and edge deployment.
-
Practical exposure to SLAM, pose estimation, optical flow, point clouds, LiDAR, and depth sensors.
-
Experience integrating ML models into Unity/Unreal is a strong advantage.
-
Knowledge of GPU acceleration and CUDA-based optimization.
-
Experience deploying ML systems using MLOps tooling (MLflow, Kubeflow, Airflow, Docker, Kubernetes).
-
Excellent communication, team collaboration, and leadership skills.