Efficient AI Researcher — On-Device Models & Inference

Bzpp; United States; Hybrid; USD 120,000 - 180,000; Part time

Job summary

LG Electronics is seeking a Contract AI Researcher to advance efficient AI for on‑device deployment. You will explore model compression, quantization, efficient inference, and next‑gen architectures, bridging research and implementation on constrained devices.

You will collaborate with CTO teams in Silicon Valley and New Jersey, publish results, contribute to IP, and build pipelines for benchmarking across language, vision, and reasoning tasks. Hybrid work in Santa Clara is offered.

Responsibilities

• 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

Job description

LG Electronics is seeking a Contract AI Researcher to advance efficient AI for on‑device deployment. You will explore model compression, quantization, efficient inference, and next‑gen architectures, bridging research and implementation on constrained devices.

You will collaborate with CTO teams in Silicon Valley and New Jersey, publish results, contribute to IP, and build pipelines for benchmarking across language, vision, and reasoning tasks. Hybrid work in Santa Clara is offered.