Principal Machine Learning Engineer

Principal Machine Learning Engineer - Multimodal AI & InferenceBangaloreFounded in 2023,by Industry veterans HQ in California,US- We are revolutionizing sustainable AI compute through intuitive software with composable siliconOverview:You will design, optimize, and deploy large multimodal models (language, vision, audio, video) to run efficiently on a compact, high-performance AI appliance capable of supporting 100B+ parameter models at real-time speeds. Your mission is to deliver state-of-the-art multimodal inference locally through advanced model optimization, quantization, and system-level integration.Key Responsibilities:1. Model Integration & Porting- Optimize large-scale foundation models (e.g., Llama, gpt-oss, Whisper, HiDream, Qwen, Wan etc) for on-device inference.- Adapt pre-trained models for multimodal tasks (text, image, audio, video, or cross-modal reasoning).- Ensure seamless interoperability between modalities — e.g., enabling the system to "see, hear, and talk" naturally.2. Model Optimization for Edge Hardware- Quantize and compress large models (4-bit or mixed precision) while maintaining high accuracy and low latency.- Implement and benchmark inference runtimes using frameworks like Llama.cpp, Ollama, vLLM, ONNX etc.- Collaborate with hardware engineers to co-design model architectures optimized for the appliance's compute fabric.3. Inference Pipeline Development- Build and maintain scalable, high-throughput inference pipelines capable of handling concurrent multimodal requests (text, audio, image, video).- Implement token streaming, caching, and scheduling strategies for real-time responses.- Develop APIs for low-latency local inference accessible via a web interface.4. Evaluation & Benchmarking- Profile and benchmark performance (throughput, latency, energy efficiency) of deployed models.- Run regression tests to validate numerical accuracy after quantization or pruning.- Define KPIs for multimodal model performance under real-world usage.5. Research & Prototyping- Investigate emerging multimodal architectures and lightweight model variants for local deployment.- Prototype hybrid models that combine LLMs, diffusion models, and ASR/TTS pipelines for advanced multimodal applications.- Stay current on state-of-the-art inference frameworks, compression techniques, and multimodal learning trends.Required Qualifications:- Strong background in deep learning and model deployment, with hands-on experience in PyTorch and/or TensorFlow.- Expertise in model optimization — quantization, pruning, distillation, or mixed-precision inference.- Practical knowledge of inference engines (vLLM, llama.cpp, ONNX Runtime or similar).- Experience deploying large models locally or on edge devices with limited memory/compute constraints.- Familiarity with multimodal model architectures — e.g., CLIP, Flamingo, LLaVA, or AudioGPT-style systems.- Strong software engineering skills (Python, C++, CUDA) and experience integrating models into production systems.- Understanding of GPU/accelerator utilization, memory bandwidth optimization, and distributed inference.Preferred Qualifications:experience-10+ years- Experience with model-parallel or tensor-parallel inference at scale.- Contributions to open-source inference frameworks or model serving systems.- Familiarity with hardware-aware training or co-optimization of neural networks and hardware.- Background in speech, vision, or multimodal ML research.- Track record of deploying models that run entirely offline or on embedded/edge systems.Contact:UdayMulya Technologiesmuday_bhaskar@yahoo.com"Mining The Knowledge Community"