{"schemaVersion":"jobsearcher.job.v1","id":"f9d9300b9ecd161546473c04","url":"https://jobsearcher.com/jobs/f9d9300b9ecd161546473c04","canonicalUrl":"https://jobsearcher.com/jobs/f9d9300b9ecd161546473c04","title":"LLM Inference Frameworks and Optimization Engineer","description":"About the Role\r\nAt Together.ai, we are building state-of-the-art infrastructure to enable efficient and scalable inference for large language models (LLMs). Our mission is to optimize inference frameworks, algorithms, and infrastructure, pushing the boundaries of performance, scalability, and cost-efficiency.\r\nWe are seeking an Inference Frameworks and Optimization Engineer to design, develop, and optimize distributed inference engines that support multimodal and language models at scale. This role will focus on low-latency, high-throughput inference, GPU/accelerator optimizations, and software-hardware co-design, ensuring efficient large-scale deployment of LLMs and vision models.\r\nThis role offers a unique opportunity to shape the future of LLM inference infrastructure, ensuring scalable, high-performance AI deployment across a diverse range of applications. If you're passionate about pushing the boundaries of AI inference, we'd love to hear from you!\r\nResponsibilities\r\nInference Framework Development and Optimization\r\nDesign and develop fault-tolerant, high-concurrency distributed inference engine for text, image, and multimodal generation models.\r\nImplement and optimize distributed inference strategies, including Mixture of Experts (MoE) parallelism, tensor parallelism, pipeline parallelism for high-performance serving.\r\nApply CUDA graph optimizations, TensorRT/TRT-LLM graph optimizations, and PyTorch-based compilation (torch.compile), and speculative decoding to enhance efficiency and scalability.\r\nSoftware-Hardware Co-Design and AI Infrastructure\r\nCollaborate with hardware teams on performance bottleneck analysis, co-optimize inference performance for GPUs, TPUs, or custom accelerators.\r\nWork closely with AI researchers and infrastructure engineers to develop efficient model execution plans and optimize E2E model serving pipelines.\r\nRequirements\r\nMust-Have:\r\nExperience: 3+ years of experience in deep learning inference frameworks, distributed systems, or high-performance computing.\r\nTechnical Skills\r\nFamiliar with at least one LLM inference frameworks (e.g., TensorRT-LLM, vLLM, SGLang, TGI(Text Generation Inference) ).\r\nBackground knowledge and experience in at least one of the following: GPU programming (CUDA/Triton/TensorRT), compiler, model quantization, and GPU cluster scheduling .\r\nDeep understanding of KV cache systems like Mooncake, PagedAttention, or custom in-house variants.\r\nProgramming: Proficient in Python and C++/CUDA for high-performance deep learning inference.\r\nOptimization Techniques\r\nDeep understanding of Transformer architectures and LLM/VLM/Diffusion model optimization .\r\nKnowledge of inference optimization , such as workload scheduling, CUDA graph, compiled, efficient kernels.\r\nSoft Skills\r\nStrong analytical problem-solving skills with a performance-driven mindset.\r\nExcellent collaboration and communication skills across teams.\r\nNice-to-Have:\r\nExperience in developing software systems for large-scale data center networks with RDMA/RoCE .\r\nFamiliar with distributed filesystem (e.g., 3FS, HDFS, Ceph ).\r\nFamiliar with open-source distributed scheduling/orchestration frameworks, such as Kubernetes (K8S) .\r\nContributions to open-source deep learning inference projects.\r\nJ-18808-Ljbffr","company":"Gravity Engineering Services","rawCompany":"gravity engineering services","city":"Millbrae","state":"CA","isRemote":false,"isActive":true,"createdAt":"2026-06-25T01:12:00.675Z","occupations":[{"code":"15-1299.08","title":"Computer Systems Engineers/Architects","slug":"computer-systems-engineers-architects"},{"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"}],"industries":[{"code":"541512","title":"Computer Systems Design Services","slug":"computer-systems-design-services"},{"code":"513210","title":"Software Publishers","slug":"software-publishers"},{"code":"541511","title":"Custom Computer Programming Services","slug":"custom-computer-programming-services"}],"jobPosting":{"@context":"https://schema.org","@type":"JobPosting","title":"LLM Inference Frameworks and Optimization Engineer","description":"About the Role\r\nAt Together.ai, we are building state-of-the-art infrastructure to enable efficient and scalable inference for large language models (LLMs). Our mission is to optimize inference frameworks, algorithms, and infrastructure, pushing the boundaries of performance, scalability, and cost-efficiency.\r\nWe are seeking an Inference Frameworks and Optimization Engineer to design, develop, and optimize distributed inference engines that support multimodal and language models at scale. This role will focus on low-latency, high-throughput inference, GPU/accelerator optimizations, and software-hardware co-design, ensuring efficient large-scale deployment of LLMs and vision models.\r\nThis role offers a unique opportunity to shape the future of LLM inference infrastructure, ensuring scalable, high-performance AI deployment across a diverse range of applications. If you're passionate about pushing the boundaries of AI inference, we'd love to hear from you!\r\nResponsibilities\r\nInference Framework Development and Optimization\r\nDesign and develop fault-tolerant, high-concurrency distributed inference engine for text, image, and multimodal generation models.\r\nImplement and optimize distributed inference strategies, including Mixture of Experts (MoE) parallelism, tensor parallelism, pipeline parallelism for high-performance serving.\r\nApply CUDA graph optimizations, TensorRT/TRT-LLM graph optimizations, and PyTorch-based compilation (torch.compile), and speculative decoding to enhance efficiency and scalability.\r\nSoftware-Hardware Co-Design and AI Infrastructure\r\nCollaborate with hardware teams on performance bottleneck analysis, co-optimize inference performance for GPUs, TPUs, or custom accelerators.\r\nWork closely with AI researchers and infrastructure engineers to develop efficient model execution plans and optimize E2E model serving pipelines.\r\nRequirements\r\nMust-Have:\r\nExperience: 3+ years of experience in deep learning inference frameworks, distributed systems, or high-performance computing.\r\nTechnical Skills\r\nFamiliar with at least one LLM inference frameworks (e.g., TensorRT-LLM, vLLM, SGLang, TGI(Text Generation Inference) ).\r\nBackground knowledge and experience in at least one of the following: GPU programming (CUDA/Triton/TensorRT), compiler, model quantization, and GPU cluster scheduling .\r\nDeep understanding of KV cache systems like Mooncake, PagedAttention, or custom in-house variants.\r\nProgramming: Proficient in Python and C++/CUDA for high-performance deep learning inference.\r\nOptimization Techniques\r\nDeep understanding of Transformer architectures and LLM/VLM/Diffusion model optimization .\r\nKnowledge of inference optimization , such as workload scheduling, CUDA graph, compiled, efficient kernels.\r\nSoft Skills\r\nStrong analytical problem-solving skills with a performance-driven mindset.\r\nExcellent collaboration and communication skills across teams.\r\nNice-to-Have:\r\nExperience in developing software systems for large-scale data center networks with RDMA/RoCE .\r\nFamiliar with distributed filesystem (e.g., 3FS, HDFS, Ceph ).\r\nFamiliar with open-source distributed scheduling/orchestration frameworks, such as Kubernetes (K8S) .\r\nContributions to open-source deep learning inference projects.\r\nJ-18808-Ljbffr","datePosted":"2026-06-25T01:12:00.675Z","dateModified":"2026-06-25T01:12:00.675Z","hiringOrganization":{"@type":"Organization","name":"Gravity Engineering Services","sameAs":"https://jobsearcher.com"},"jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"Millbrae","addressRegion":"CA","addressCountry":"US"}},"identifier":{"@type":"PropertyValue","name":"JobSearcher","value":"f9d9300b9ecd161546473c04"},"url":"https://jobsearcher.com/jobs/f9d9300b9ecd161546473c04"}}