SDE II - Gen AI

Glance; Bengaluru; On-site; INR 2,500,000 - 4,500,000; Full time

Job summary

Glance is seeking an engineer to work across Generative AI, model fine-tuning, optimization, and on-device deployment for iOS and Android. The role focuses on running large models under latency, memory, battery, and thermal constraints to deliver production-ready inference on consumer devices.

You will work on image generation, model optimization, and deployment pipelines using PyTorch, ONNX, and TensorRT, with hands-on experience in Core ML and TFLite for on-device performance.

Qualifications

• 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.
• Knowledge of inference runtimes such as ONNX, TensorRT, Core ML, TFLite/LiteRT, or ONNX Runtime.

Responsibilities

• 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.

Skills

Python

PyTorch

deep learning

Computer Vision

Generative AI

LoRA

model optimization

quantization

debugging

profiling

software engineering fundamentals

Tools

ONNX

ONNX Runtime

TensorRT

Core ML

TFLite/LiteRT

Job description

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 withPython, 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
• 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:

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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