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# AI Researcher (Efficient AI)

- **Company:** Inc., Lg
- **Location:** Santa Clara, CA, United States
- **Salary:** $174,990.0 - $189,987.0
- **Contract:** Temporary to permanent
- **Skills:** Artificial Intelligence, Software Code 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, GGUF, Information Technology, SGLang, TensorRT, SQL Server Management Studio (SSMS), VLLM, llama.cpp
- **Published:** October 27, 2026
- **Apply:** https://www.adzuna.com/details/5943330539

## 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.