[Skip to main content](#main-content) [OpenNash Jobs](https://jobs.opennash.com/) [Hiring report](/reports/hiring/) · [All NVIDIA jobs](https://jobs.opennash.com/companies/nvidia/) · [Search jobs](/search/) NVIDIA · Multiple locations # Senior Deep Learning Software Engineer, Inference Multiple locations Senior individual contributor Software Engineering Listed on NVIDIA’s official careers site. Posted September 24, 2026. Check that it is still open and apply there. [Apply on NVIDIA’s careers site →](https://nvidia.wd5.myworkdayjobs.com/NVIDIAExternalCareerSite/job/Netherlands-Remote/Senior-Deep-Learning-Software-Engineer--Inference_JR2023106-1) [OpenNash Jobs](https://jobs.opennash.com/) / [NVIDIA](https://jobs.opennash.com/companies/nvidia/) / Senior Deep Learning Software Engineer, Inference ## Job description NVIDIA seeks a Senior Software Engineer specializing in Deep Learning Inference for our growing team. As a key contributor, you will help design, build, and optimize the GPU-accelerated software that powers today’s most sophisticated AI applications. Our team is responsible for developing and maintaining high-performance deep learning frameworks, including SGLang and vLLM, which are at the forefront of efficient large-scale model serving and inference. You will play a central role in improving these platforms, facilitating smooth deployment and serving of groundbreaking language models. You’ll work closely with the deep learning community to implement the latest algorithms for public release in frameworks like SGLang and vLLM, as well as other DL frameworks. Your work will focus on identifying and driving performance improvements for state-of-the-art LLM and Generative AI models across NVIDIA accelerators, from datacenter GPUs to edge SoCs. You'll bring to bear open-source tools and plugins—including CUTLASS, OAI Triton, NCCL, and CUDA kernels—to implement and optimize model serving pipelines. ### What you'll be doing Performance optimization, analysis, and tuning of DL models in various domains like LLM, Multimodal and Generative AI. Scale performance of DL models across different architectures and types of NVIDIA accelerators. Contribute features and code to NVIDIA’s inference libraries, vLLM and SGLang, FlashInfer and LLM software solutions. Work with cross-collaborative teams across frameworks, NVIDIA libraries and inference optimization innovative solutions. ### What we need to see Masters or PhD or equivalent experience in relevant field (Computer Engineering, Computer Science, EECS, AI). 5+ years of relevant software development experience. Excellent C/C++ programming and software design skills. SW Agile skills are helpful and Python experience is a plus. Prior experience with training, deploying or optimizing the inference of DL models in production is a plus. Prior background with performance modeling, profiling, debug, and code optimization or architectural knowledge of CPU and GPU is a plus. ### Ways to stand out from the crowd Contribute to Deep Learning Software projects, such as PyTorch, vLLM, and SGLang to drive advancements in the field. Experience with Multi-GPU Communications (NCCL, NVSHMEM) Experience building and shipping products to enterprise customers. GPU programming experience (CUDA, OAI TRITON or CUTLASS). NVIDIA is at the forefront of breakthroughs in Artificial Intelligence, High-Performance Computing, and Visualization. Our teams are composed of driven, innovative professionals dedicated to pushing the boundaries of technology. We offer highly competitive salaries, an extensive benefits package, and a work environment that promotes diversity, inclusion, and flexibility. As an equal opportunity employer, we are committed to fostering a supportive and empowering workplace for all Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. ### For Poland The base salary range is 221,250 PLN - 383,500 PLN for Level 3, and 292,500 PLN - 507,000 PLN for Level 4. ## At a glance - Posted by the employer: Sep 24, 2026 - First seen by OpenNash: Aug 20, 2026 - Last checked: Oct 20, 2026 - Listed for: 61 days - Location: Multiple locations[Apply on NVIDIA’s careers site →](https://nvidia.wd5.myworkdayjobs.com/NVIDIAExternalCareerSite/job/Netherlands-Remote/Senior-Deep-Learning-Software-Engineer--Inference_JR2023106-1) ## More jobs at NVIDIA ### [AI Developer Technology Engineering Intern - 2027](https://jobs.opennash.com/companies/nvidia/jobs/4016fa8723a48261ac1e/) ### [Developer Relations Manager - Al Algorithms, Benchmarks, OSS Datasets](https://jobs.opennash.com/companies/nvidia/jobs/5895e7e294e5f2e6774e/) ### [Developer Relations Manager – CAE and CFD](https://jobs.opennash.com/companies/nvidia/jobs/65c00b21157b64541f20/) ### [Developer Relations Manager – Enterprise AI](https://jobs.opennash.com/companies/nvidia/jobs/ae9d28c5aadcab813ae0/) ### [Developer Relations Manager, Retail & Media Entertainment](https://jobs.opennash.com/companies/nvidia/jobs/81313bae6fd4dd20aba2/) ## Similar jobs at other companies ### [Z3_Senior Deep Learning Engineer (Engineering / 3D AI)](https://jobs.opennash.com/companies/ntt/jobs/4060faf277f9048e0d07/) NTT · Bangalore, KA, IN, India ### [Intern- Deep Learning R&D for Automated Driving](https://jobs.opennash.com/companies/qualcomm/jobs/1bd901b22e6940c97f17/) Qualcomm · Seoul, Korea, Republic of ### [Senior Deep Learning Researcher – Model Efficiency - Qualcomm - Amsterdam](https://jobs.opennash.com/companies/qualcomm/jobs/f9142cc85ab1ff56cc6e/) Qualcomm · Amsterdam Drenthe, North Holland, Netherlands ### [Internship in Domain Adaptation for Time-Series Using Deep-Learning f/m/d (80-100%)](https://jobs.opennash.com/companies/abb/jobs/486d9fb58cd0bd701a30/) ABB · Baden-Daettwil, Aargau, Switzerland ### [Process Enginee AI / Machine Learning / Deep Learning](https://jobs.opennash.com/companies/applied-materials/jobs/ddbcbad1122f6dd2d4b6/) Applied Materials · Treviso, ITA [Software Engineering jobs](/functions/software-engineering/) OpenNash lists jobs from employers’ own careers sites and does not handle applications. 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