AI Researcher - Efficient AI (Contractor)
$174,990–$189,987 year
HybridSanta Clara, California, United States
Job Summary
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 for on-device enablement, devise approaches for long-context inference and KV cache compression, and develop gradient-free methods for model merging. Implement emerging efficient architectures and modules, prototype inference-time optimization techniques, and build experimental pipelines for standardized benchmarks. Contribute to publications, technical reports, open-source releases, and IP submissions. This one-year contract role at LG's Emerging Technology Lab in Santa Clara, CA (hybrid) supports advanced AI capabilities across AI PCs, edge devices, robotics, and intelligent vehicle systems.
Required Qualifications
- M.S. or Ph.D. in Computer Science, Computer Engineering, Machine Learning, Mathematics, or a related technical field
- 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
Desired Qualifications
- Relevant post-graduate research and/or industry experience
- 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
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