Glance

Glance is an intelligent shopping agent, redefining the commerce experience. Powered by proprietary agentic intelligence and generative AI, Glance delivers a hyper-personalized consumer experience across mobile and TV — shaping the new era of shopping. Glance is operated by Glance InMobi Pte. Ltd., a non-consolidated subsidiary of global technology leader InMobi, and is backed by Mithril Capital, Google and Jio Platforms. To learn more, visit glance.com.

InMobi

InMobi Group is a global technology company shaping the future of agentic commerce and advertising. Through its ecosystem of businesses — including InMobi Advertising and flagship consumer platform Glance — InMobi leverages data, machine learning, and generative AI to help brands reach audiences more precisely and consumers discover products more intuitively. Glance, which is pioneering new models of agentic commerce, is owned and operated by Glance InMobi Pte. Ltd., a non-consolidated subsidiary of InMobi Pte. Ltd.

InMobi Advertising

InMobi Advertising, part of global technology company InMobi, is an agentic advertising platform helping brands and merchants achieve their business outcomes. Through its proprietary intelligence, AI-led solutions, and vast consumer reach — including flagship consumer platform Glance — InMobi Advertising delivers the omnichannel performance defining what's next in advertising and commerce. Glance is owned and operated by Glance InMobi Pte. Ltd., a non-consolidated subsidiary of InMobi Pte. Ltd. To learn more, visit advertising.inmobi.com.

We’re looking for an engineer who can work across Generative AI, model fine-tuning, optimization, and on-device deployment for iOS and Android.

This role is ideal for someone who enjoys taking models beyond experimentation and making them actually work on consumer devices under real constraints such as latency, memory, battery, thermal limits, and model size.

What you’ll work on

Image Generation, VTON, identity-preserving and reference-based generation
LoRA training, fine-tuning, personalization, and adapter-based techniques
PyTorch-based training and experimentation
Diffusion / Transformer-based image generation architectures
Dataset preparation, training pipelines, and evaluation
Model conversion and deployment using ONNX / ONNX Runtime
Inference optimization using TensorRT
Quantization using FP16 / INT8 / INT4 and other optimization techniques
On-device inference on iOS using Core ML and Android using TFLite/LiteRT, ONNX Runtime, or similar runtimes
Performance profiling and debugging across CPU, GPU, NPU / ANE
Reducing inference latency, peak memory usage, and model footprint
Building production-ready model pipelines from training to device deployment

What we’re looking for

2-5 Years of Experience
Strong experience with Python, PyTorch, and deep learning
Hands-on experience with Computer Vision / Generative AI
Experience training or fine-tuning image-generation models
Good understanding of LoRA, model optimization, and quantization
Strong knowledge of inference runtimes such as ONNX, TensorRT, Core ML, TFLite/LiteRT, or ONNX Runtime
Strong debugging, profiling, and software engineering fundamentals

Good to have

Experience with FLUX, Stable Diffusion, SDXL, ControlNet, IP-Adapter, VAE, CLIP, or similar architectures
CUDA / GPU optimization
Metal / Apple Neural Engine
Android NNAPI / GPU delegates
C++ / Swift / Kotlin
Distributed or multi-GPU training
Model compression, pruning, and knowledge distillation

We’re especially interested in engineers who can think end-to-end:

Data → Training → Fine-tuning → Evaluation → Quantization → Model Conversion → Runtime Optimization → On-Device Deployment → Production Monitoring

If you enjoy solving problems like “How do we make a large generative model run fast, efficiently, and reliably on a phone?”, this role should be exciting.

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