Machine Learning Engineer

Role : Machine Learning Engineer Augmented Reality

Location : Little Rock, AR

Experience : 12+ Years About the Role

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.

Key Responsibilities

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.

Required Skills & Experience

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.

Preferred Qualifications

Master's or PhD in Computer Science, AI / ML, Robotics, or related field.

Experience working with ARCore, ARKit, Vision Pro, Meta Quest, HoloLens, or similar platforms.

Cloud experience with AWS, GCP, or Azure and familiarity with real-time streaming data pipelines.