Machine Learning Intern

Machine Learning Engineer Edge AI & Computer Vision

Company: InfiVR

Location: On-site

Employment Type: Internship

About InfiVR

InfiVR builds advanced VR/AR and AI-powered enterprise solutions. We specialize in real-time, on-device intelligence for immersive training, industrial automation, and smart assistance systems.

We are hiring a Machine Learning Engineer with strong expertise in Edge AI, small LLMs, and Computer Vision, capable of building optimized models that run efficiently on AR glasses, mobile devices, and embedded systems.

Role Overview

You will design, train, optimize, and deploy machine learning models for on-device inference with strict latency and memory constraints.

Your focus areas:

• Small LLMs / SLMs (<1B parameters)
• Computer Vision models for real-time applications
• Model compression & quantization
• Edge deployment & performance optimization with tools like Qualcomm AI hub

Key Responsibilities

• Train and fine-tune small LLMs for domain-specific applications.
• Use of tools like Qualcomm AI Hub.
• Develop and optimize small Computer Vision models for image explanation and description like smolvlm.
• Apply quantization, pruning, distillation, LoRA/QLoRA.
• Deploy models using TensorFlow Lite, ONNX Runtime, TensorRT, or PyTorch Mobile.
• Optimize models for low-latency, low-memory environments.
• Build real-time AI pipelines for AR/VR and Android-based devices.
• Work closely with Unity, mobile, Android native module, and backend teams for AI integration.

Required Skills

• Strong Python, PyTorch and/or TensorFlow expertise.
• Hands-on experience with offline edge deployment and model optimization.
• Experience with YOLO, MobileNet, EfficientNet or similar lightweight architectures.
• Knowledge of transformer architectures and small LLM fine-tuning.
• Experience deploying models on Android, embedded systems, or edge hardware.
• Strong understanding of memory, compute, and inference optimization.

What Were Looking For

• Someone who has deployed real on-device AI systems (not cloud training).
• Strong performance optimization mindset.
• Comfortable working in R&D-driven, fast-moving environments.