AI Researcher - Efficient AI (Contractor)

LG Electronics USA; Santa Clara (CA); Hybrid; USD 120,000 - 180,000; Full time

Job summary

LG Electronics USA's Emerging Technology Lab in Santa Clara, CA seeks a Contract AI Researcher focused on Efficient AI. You will explore model compression, quantization, and on-device inference to make LLMs and multimodal models faster and lighter for real-world applications.

The role blends research with hands-on implementation, collaborating with researchers to publish findings, contribute to IP, and drive measurable improvements across LG's future products, including AI PCs, edge devices, and

Qualifications

• MS/PhD in Computer Science, Engineering, Mathematics or related field.
• Experience in ML, efficient AI, model optimization, or AI systems.
• Strong Python and PyTorch experience; ability to read and implement research papers.

Responsibilities

• Research methods to improve model efficiency and deployment on constrained devices.
• Optimize LLMs/SLMs/VLMs and multimodal workloads across post-training and inference.
• Propose compression methods (PTQ, QAT, pruning) for on-device AI.
• Prototype inference-time optimization and kernel-level enhancements.
• Contribute to publications, IP disclosures, and open-source efforts.

Skills

Python

PyTorch

ML Research

Model Optimization

On-device AI

Prototyping

Education

MS/PhD in CS/ML

Tools

llama.cpp

GGUF

vLLM

TensorRT-LLM

CUDA

Job description

Step into the innovative world of LG Electronics. As a global leader in technology, LG Electronics is dedicated to creating innovative solutions for a better life. Our brand promise, 'Life's Good', embodies our commitment to ensuring a happier life for all. We have a rich history spanning over six decades and a global presence in over 290 locations.Our diverse portfolio includes Home Appliance Solutions, Media Entertainment Solutions, Vehicle Solutions, and Eco Solutions. Our management philosophy, "Jeong-do Management," embodies our commitment to high ethical standards and transparent operations. Grounded in the principles of 'Customer-Value Creation' and 'People-Oriented Management', these values shape our corporate culture, fostering creativity, diversity, and integrity. At LG, we believe in the power of collective wisdom through an inclusive work environment.Join us and become a part of a company that is shaping the future of technology.At LG, we strive to make Life Good for Everyone.

About the Team

LG's Emerging Technology Lab (ETL) is the catalyst for technological innovation within LG's CTO organization. Located in the Silicon Valley and New Jersey, we drive excellence across CTO organizations and business units by pioneering in select emerging technology areas. As the Center of Excellence (CoE), we define and shape key technology domains, setting strategic directions that foster impactful internal and external partnerships to deliver measurable business value.

About the Opportunity

We are seeking a Contract AI Researcher - Efficient AI to join LG's Emerging Technology Lab in Santa Clara, CA (hybrid). This is an exciting opportunity to work at the forefront of AI efficiency research, developing technologies that make modern LLMs, VLMs, multimodal models, and AI agents faster, smaller, and more deployable in real-world environments.

In this role, you will explore cutting-edge areas such as model compression, quantization, efficient inference, reasoning optimization, and next-generation AI architectures. Your work will help enable advanced AI capabilities across LG's future products and platforms, including AI PCs, edge devices, robotics, and intelligent vehicle systems.

The ideal candidate enjoys bridging research and implementation, transforming ideas from the latest scientific literature into working prototypes and measurable improvements. You will have the opportunity to collaborate with experienced researchers, contribute to publications and intellectual property, and help shape the future of efficient, on-device AI.

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 where appropriate.

Required Qualifications

• 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.

Contract

This is expected to be a one-year contract position, with the potential for extension based on business needs and performance.

Privacy Notice

Privacy Notice to California Applicants

Applicants who need assistance or a reasonable accommodation during the hiring process may contact our team by phone at: 973-477-7090 or [e-mail]. This email and phone number will only reply to accommodation requests and is not intended for general employment inquiries.

All qualified applicants will be considered for employment without regard to race, color, religion, sex (including pregnancy, sexual orientation, or gender identity), national origin, age, disability, genetic information, protected veteran status, or any other characteristic protected by applicable federal, state, or local law.

In addition to the above, LG believes that pay transparency is a key part of diversity, equity, and inclusion. Our salary ranges take into account many factors in making compensation decisions including but not limited to skillset, experience, licensure, certifications, internal equity, and other business needs. While we consider geographic pay differentials in final offers, because we operate in many geographies where applicable, the salary range listed may not reflect all geographic differentials applied.