{"schemaVersion":"jobsearcher.job.v1","id":"c48ec64f827c8b74c15e3454","url":"https://jobsearcher.com/jobs/c48ec64f827c8b74c15e3454","canonicalUrl":"https://jobsearcher.com/jobs/c48ec64f827c8b74c15e3454","title":"Staff Machine Learning Engineer - LLM Quantization & Deployment","description":"Staff Machine Learning Engineer - LLM Quantization & DeploymentXPENG is a leading smart technology company at the forefront of innovation, integrating advanced AI and autonomous driving technologies into its vehicles, including electric vehicles (EVs), electric vertical take-off and landing (eVTOL) aircraft, and robotics. With a strong focus on intelligent mobility, XPENG is dedicated to reshaping the future of transportation through cutting-edge R&D in AI, machine learning, and smart connectivity.\r\nOur mission is to build strong foundation for LLM deployment and quality sign-off for next-gen XPENG Turing AI chip. This includes and is not limited to: LLM model fine tuning, PTQ, QAT, on-vehicle inference and related fields.\r\nKey ResponsibilitiesDevelop VLA inference models, ensure numerical consistency with training models, and productionize LLM quantization methods, including PTQ, QAT, mixed-precision inference, INT8, FP4, and lower-bit techniques.\r\nDevelop production-quality Python code with strong testing, observability, reproducibility, and failure handling.\r\nBuild robust model export, calibration, benchmarking, validation, and deployment pipelines.\r\nEngage early with the VLA model research team to establish performance estimates and prove model feasibility.\r\nCurate evaluation datasets and establish a comprehensive metric suite to systematically benchmark VLA performance.\r\nAnalyze numerical errors, accuracy regressions, and performance trade-offs.\r\nDevelop PTQ and QAT orchestration workflows.\r\nServe as the primary interface with field-testing and simulation teams for issue triage and autonomous driving performance sign-off.\r\nCollaborate with the in-vehicle software team on latency analysis and issue triage.\r\nCollaborate with the training infrastructure team to develop QAT and model distillation.\r\nBasic QualificationsMaster in CS/CE/EE, or equivalent, with 3-5 years of industry experience.\r\nStrong understanding of Transformer architectures and LLM inference.\r\nHands-on experience quantizing or deploying deep learning models in production.\r\nProficiency with PyTorch and at least one inference or compilation stack.\r\nStrong Python programming and software engineering skills.\r\nAbility to work effectively across research, systems, infrastructure, and product teams.\r\nExcellent communication and problem-solving skills, with the ability to thrive in a fast-paced and collaborative environment.\r\nPreferred QualificationsExperience with weight-only, activation, KV-cache, dynamic, static, or mixed-precision quantization.\r\nExperience with AWQ, GPTQ, SmoothQuant, or related methods.\r\nStrong numerical analysis and systems engineering skills.\r\nExperience with one or more LLM runtimes, such as TensorRT-LLM, vLLM, SGLang, llama.cpp, ONNX Runtime, TVM, MLIR, or custom runtimes.\r\nExperience deploying LLMs on resource-constrained or heterogeneous hardware.\r\nContributions to model optimization, inference, compiler, or serving projects.\r\nPublications at NeurIPS, ICML, ICLR, ACL, or related conferences.\r\nWhat We ProvideA fun, supportive and engaging environment.\r\nInfrastructures and computational resources to support your work.\r\nOpportunity to work on cutting edge technologies with the top talents in the field.\r\nOpportunity to make a significant impact on the transportation revolution by the means of advancing autonomous driving.\r\nCompetitive compensation package.\r\nSnacks, lunches, dinners, and fun activities.\r\nThe base salary range for this full-time position is $215,280 - $364,320, in addition to bonus, equity and benefits. Our salary ranges are determined by role, level, and location. The range displayed on each job posting reflects the minimum and maximum target for new hire salaries for the position across all US locations. Within the range, individual pay is determined by work location and additional factors, including job-related skills, experience, and relevant education or training.\r\nWe are an Equal Opportunity Employer. It is our policy to provide equal employment opportunities to all qualified persons without regard to race, age, color, sex, sexual orientation, religion, national origin, disability, veteran status or marital status or any other prescribed category set forth in federal or state regulations.#J-18808-Ljbffr","company":"Xpeng","rawCompany":"xpeng","city":"Santa Clara","state":"CA","isRemote":false,"isActive":false,"createdAt":"2026-08-21T01:13:54.965Z","occupations":[{"code":"15-1252.00","title":"Software Developers","slug":"software-developers"},{"code":"15-1221.00","title":"Computer and Information Research Scientists","slug":"computer-and-information-research-scientists"},{"code":"15-2051.00","title":"Data Scientists","slug":"data-scientists"}],"industries":[{"code":"541511","title":"Custom Computer Programming Services","slug":"custom-computer-programming-services"},{"code":"513210","title":"Software Publishers","slug":"software-publishers"},{"code":"336110","title":"Automobile and Light Duty Motor Vehicle Manufacturing","slug":"automobile-and-light-duty-motor-vehicle-manufacturing"}],"jobPosting":{"@context":"https://schema.org","@type":"JobPosting","title":"Staff Machine Learning Engineer - LLM Quantization & Deployment","description":"Staff Machine Learning Engineer - LLM Quantization & DeploymentXPENG is a leading smart technology company at the forefront of innovation, integrating advanced AI and autonomous driving technologies into its vehicles, including electric vehicles (EVs), electric vertical take-off and landing (eVTOL) aircraft, and robotics. With a strong focus on intelligent mobility, XPENG is dedicated to reshaping the future of transportation through cutting-edge R&D in AI, machine learning, and smart connectivity.\r\nOur mission is to build strong foundation for LLM deployment and quality sign-off for next-gen XPENG Turing AI chip. This includes and is not limited to: LLM model fine tuning, PTQ, QAT, on-vehicle inference and related fields.\r\nKey ResponsibilitiesDevelop VLA inference models, ensure numerical consistency with training models, and productionize LLM quantization methods, including PTQ, QAT, mixed-precision inference, INT8, FP4, and lower-bit techniques.\r\nDevelop production-quality Python code with strong testing, observability, reproducibility, and failure handling.\r\nBuild robust model export, calibration, benchmarking, validation, and deployment pipelines.\r\nEngage early with the VLA model research team to establish performance estimates and prove model feasibility.\r\nCurate evaluation datasets and establish a comprehensive metric suite to systematically benchmark VLA performance.\r\nAnalyze numerical errors, accuracy regressions, and performance trade-offs.\r\nDevelop PTQ and QAT orchestration workflows.\r\nServe as the primary interface with field-testing and simulation teams for issue triage and autonomous driving performance sign-off.\r\nCollaborate with the in-vehicle software team on latency analysis and issue triage.\r\nCollaborate with the training infrastructure team to develop QAT and model distillation.\r\nBasic QualificationsMaster in CS/CE/EE, or equivalent, with 3-5 years of industry experience.\r\nStrong understanding of Transformer architectures and LLM inference.\r\nHands-on experience quantizing or deploying deep learning models in production.\r\nProficiency with PyTorch and at least one inference or compilation stack.\r\nStrong Python programming and software engineering skills.\r\nAbility to work effectively across research, systems, infrastructure, and product teams.\r\nExcellent communication and problem-solving skills, with the ability to thrive in a fast-paced and collaborative environment.\r\nPreferred QualificationsExperience with weight-only, activation, KV-cache, dynamic, static, or mixed-precision quantization.\r\nExperience with AWQ, GPTQ, SmoothQuant, or related methods.\r\nStrong numerical analysis and systems engineering skills.\r\nExperience with one or more LLM runtimes, such as TensorRT-LLM, vLLM, SGLang, llama.cpp, ONNX Runtime, TVM, MLIR, or custom runtimes.\r\nExperience deploying LLMs on resource-constrained or heterogeneous hardware.\r\nContributions to model optimization, inference, compiler, or serving projects.\r\nPublications at NeurIPS, ICML, ICLR, ACL, or related conferences.\r\nWhat We ProvideA fun, supportive and engaging environment.\r\nInfrastructures and computational resources to support your work.\r\nOpportunity to work on cutting edge technologies with the top talents in the field.\r\nOpportunity to make a significant impact on the transportation revolution by the means of advancing autonomous driving.\r\nCompetitive compensation package.\r\nSnacks, lunches, dinners, and fun activities.\r\nThe base salary range for this full-time position is $215,280 - $364,320, in addition to bonus, equity and benefits. Our salary ranges are determined by role, level, and location. The range displayed on each job posting reflects the minimum and maximum target for new hire salaries for the position across all US locations. Within the range, individual pay is determined by work location and additional factors, including job-related skills, experience, and relevant education or training.\r\nWe are an Equal Opportunity Employer. It is our policy to provide equal employment opportunities to all qualified persons without regard to race, age, color, sex, sexual orientation, religion, national origin, disability, veteran status or marital status or any other prescribed category set forth in federal or state regulations.#J-18808-Ljbffr","datePosted":"2026-08-21T01:13:54.965Z","dateModified":"2026-08-21T01:13:54.965Z","hiringOrganization":{"@type":"Organization","name":"Xpeng","sameAs":"https://jobsearcher.com"},"jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"Santa Clara","addressRegion":"CA","addressCountry":"US"}},"identifier":{"@type":"PropertyValue","name":"JobSearcher","value":"c48ec64f827c8b74c15e3454"},"url":"https://jobsearcher.com/jobs/c48ec64f827c8b74c15e3454"}}