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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # AI Researcher (Efficient AI) - **Company:** LG Electronics - **Location:** Santa Clara, CA, United States - **Salary:** $174,990.0 - $189,987.0 - **Contract:** Temporary to permanent - **Skills:** Artificial Intelligence, Program Optimization, Computer Programming, Computer Engineering, Python (Programming Language), Machine Learning, Open Source Technology, Pytorch, Delivery Pipeline, Large Language Models, Multi-Agent Systems, Deep Learning, Generative AI, Information Technology, TensorRT, SQL Server Management Studio (SSMS) - **Published:** September 13, 2026 - **Apply:** https://jobs.localjobnetwork.com/apply/add/87844725/1 ## About the Role * M.S. or Ph.D. in Computer Science, Computer Engineering, Machine Learning, Mathematics, or a related technical field. Relevant post-graduate research and/or industry experience is preferred but not required. * Research or engineering experience in ML, efficient AI, model optimization, or AI systems. * Strong programming ability in Python and experience with PyTorch or a comparable deep learning framework. * Hands-on experience with modern LLMs, SLMs, VLMs, multimodal models, or generative AI systems. * Ability to read research papers, implement technical methods, run experiments, and communicate results clearly. * Comfortable working in a fast-moving and ambiguous technical environment. * Strong written and verbal communication skills for reports, presentations, demos, and technical documentation. Ways to Stand Out * Publications in reputable venues in ML and/or systems space (e.g., ICML, ICLR, NeurIPS, ACL, COLM, EMNLP, MLSys, MICRO, etc). * Experience with modern LLM/VLM inference and deployment frameworks such as llama.cpp, GGUF, vLLM, SGLang, TensorRT-LLM, or related systems. * Experience with efficiency-aware post-training or finetuning methods such as PTQ, QAT, LoRA, distillation, instruction tuning, DPO, OPD, RLVR, or reasoning-oriented adaptation. * Experience with low-level kernel implementations and on-device acceleration. * Familiarity with emerging architectures such as MoE, SSMs, hybrid attention, or Looped Transformers. * Experience with AI-assisted optimization, multi-agent systems, or agentic-based workflows for Efficient AI and hardware/software co-design. ## Description * Research, prototype, and implement AI methods that improve model efficiency, inference performance, and deployment feasibility on constrained devices. * Optimize modern LLMs, SLMs, VLMs, multimodal models, and agentic workloads across post-training, inference, and deployment workflows. * Propose and evaluate novel compression methods (PTQ, QAT, pruning, low-rank approximation, etc) for on-device LLM/VLM enablement. * Devise approaches to address challenges related to long-context inference and KV cache compression in the context of reasoning and agentic applications. * Develop gradient-free and backpropagation-free methods for model merging, compression, and efficiency-driven optimization. * Implement and evaluate emerging efficient architectures and modules, including MoE, SSMs, hybrid models, Looped Transformers, etc. * Prototype inference-time optimization methods such as speculative decoding, constrained decoding, low-latency generation, and kernel-level optimization. * Build experimental pipelines, perform evaluations on standardized language, vision, reasoning, and agentic benchmarks. * Contribute to publications, technical reports, open-source releases, invention disclosures, and IP submissions where appropriate. ## Related Videos - [Tour de Force: Open-Source LLM Inference Optimization from Simple to Sophisticated](https://www.wearedevelopers.com/videos/100099-tour-de-force-open-source-llm-inference-optimization-from-simple-to-sophisticated) - [Getting Started with Machine Learning](https://www.wearedevelopers.com/videos/260-getting-started-with-machine-learning) - [Photonic Computing: Programming a New Class of AI Accelerators (incl. Live Coding)](https://www.wearedevelopers.com/videos/100196-photonic-computing-programming-a-new-class-of-ai-accelerators-incl-live-coding) - [How AI Models Get Smarter](https://www.wearedevelopers.com/videos/1374-how-ai-models-get-smarter) - [Geometric deep learning for drug discovery](https://www.wearedevelopers.com/videos/264-geometric-deep-learning-for-drug-discovery) - [Efficient deployment and inference of GPU-accelerated LLMs​](https://www.wearedevelopers.com/videos/929-efficient-deployment-and-inference-of-gpu-accelerated-llms) ## Related Articles - [What Are Large Language Models?](https://www.wearedevelopers.com/magazine/304-what-are-large-language-models) - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [MLOps And AI Driven Development](https://www.wearedevelopers.com/magazine/82-mlops-and-ai-driven-development) - [Got AI ideas but no money? Here are 10 free ways to level up your AI skills with Google Cloud](https://www.wearedevelopers.com/magazine/600-got-ai-ideas-but-no-money-here-are-10-free-ways-to-level-up-your-ai-skills-with-google-cloud) - [MLOps – What’s the deal behind it?](https://www.wearedevelopers.com/magazine/125-mlops-what-s-the-deal-behind-it) - [What Industries Outside of AI Are Hiring The Most AI Experts?](https://www.wearedevelopers.com/magazine/98-what-industries-outside-of-ai-are-hiring-the-most-ai-experts)