{"id":2933834,"url":"https://alion.io/job/nvidia-genai-algorithms-intern-model-optimization-for-inference","title":"GenAI Algorithms Intern – Model Optimization for Inference","company":{"id":6,"name":"NVIDIA","domain":"nvidia.com","url":"https://alion.io/company/nvidia","size_band":"5000+","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Workday","truth_index":{"grade":"B","score":80,"open_postings":814,"ghost_share":0.023,"stale_share":0.732,"repost_share":0.044,"time_to_fill_p50_days":35,"computed_at":"2026-10-23T05:54:15Z"}},"role":"Early Careers","role_family":"Early Careers","seniority":"intern","employment_type":"internship","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Warsaw, Poland","Poland"],"countries":["PL"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":28000,"max_usd":54000,"period":"year","method":"global_role_cell_scaled_by_country","sample_n":768},"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"CUDA","optional":false},{"name":"Diffusion Models","optional":false},{"name":"Fine-tuning","optional":false},{"name":"Hugging Face","optional":false},{"name":"JAX","optional":false},{"name":"Knowledge Distillation","optional":false},{"name":"KV Cache","optional":false},{"name":"Machine Learning","optional":false},{"name":"Megatron-LM","optional":false},{"name":"Model Distillation","optional":false},{"name":"Multimodal AI","optional":false},{"name":"NVIDIA NeMo","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"Quantization","optional":false},{"name":"SGLang","optional":false},{"name":"Speculative Decoding","optional":false},{"name":"TensorRT-LLM","optional":false},{"name":"Triton","optional":false},{"name":"vLLM","optional":false},{"name":"TensorRT","optional":true}],"status":"live","first_seen_at":"2026-10-20T15:30:32Z","employer_posted_date":"2026-10-20","last_verified_at":"2026-10-23T06:25:12Z","board_verified":true,"closed_at":null,"days_open":2,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":2},"description":"NVIDIA is at the forefront of the generative AI revolution! The Algorithmic Model Optimization Team focuses on optimizing generative AI models such as large language models (LLMs), vision-language models (VLMs), and diffusion models for maximal inference efficiency using techniques ranging from quantization, speculative decoding, sparsity, distillation, pruning to neural architecture search, and automated deployment strategies.\nOur work includes conducting applied research to improve model efficiency as well as developing an innovative open-source model optimization platform (NVIDIA Model Optimizer). Our software is used both internally across NVIDIA and externally by research and engineering teams alike developing best-in-class AI models.\nWe are seeking a GenAI Algorithms Intern to help optimize state-of-the-art generative AI models like LLMs, VLMs, multimodal and diffusion models. In this role, you will design, implement, and productionize model optimization algorithms for inference and deployment on NVIDIA’s latest hardware platforms. The focus is on ease of use, compute and memory efficiency, and achieving the best accuracy-performance tradeoffs through software-hardware co-design.\nYour work will span multiple layers of the AI software stack-ranging from algorithm design to integration-within NVIDIA’s ecosystem (Model Optimizer, NeMo/Megatron, TensorRT-LLM) and open-source frameworks (PyTorch, Hugging Face, vLLM, SGLang). You may also dive deeper into GPU-level optimization, including custom kernel development with CUDA and Triton.\nWhat you’ll be doing:\nHelp design and build modular, scalable model optimization software platforms that deliver exceptional user experiences while supporting diverse AI models and optimization techniques.\nExplore, develop, and integrate innovative deep learning optimization algorithms (quantization, speculative decoding, sparsity, distillation, pruning, neural architecture search) into NVIDIA's AI software stack.\nDeploy optimized models into leading open-source inference frameworks and contribute specialized APIs, model-level optimizations, and new features tailored to the latest NVIDIA hardware capabilities.\nCollaborate with NVIDIA teams on model optimization solutions for customer use cases, aiming for optimal end-to-end workflows and balanced accuracy-performance trade-offs.\nConduct GPU kernel-level profiling to identify and capitalize on hardware and software optimization opportunities (e.g., efficient attention kernels, KV cache optimization, parallelism strategies).\nWhat we need to see:\nPursuing a Master's or PhD in Computer Science, AI, Applied Math, or a related field.\nStrong deep learning background and hands-on experience with language models.\nSolid foundation in algorithms and programming.\nProficiency in Python, PyTorch, and modern ML frameworks/tools.\nGood written and verbal communication skills, with the ability to work both independently and collaboratively in a fast-paced environment.\nWays to stand out from the crowd:\nExperience with model optimization and efficiency (e.g., quantization, distillation, pruning).\nContributions to PyTorch, JAX, vLLM, SGLang, or other machine learning training and inference frameworks.\nHands-on experience training or fine-tuning generative AI models on large-scale GPU clusters.\nKnowledge of GPU architectures and compilation stacks, and the ability to analyze and debug end-to-end performance.\nFamiliarity with NVIDIA’s deep learning SDKs (Model Optimizer, NeMo).\nIncreasingly known as “the AI computing company” and widely considered to be one of the technology world’s most desirable employers, NVIDIA offers highly competitive salaries and a comprehensive benefits package. Are you creative, motivated, and love a challenge? If so, we want to hear from you! Come, join our model optimization group, where you can help build real-time, cost-effective computing platforms driving our success in this exciting and rapidly-growing field.\nNVIDIA is committed to fostering a diverse work environment and proud to be an equal opportunity employer. As we highly value diversity in our current and future employees, we do not discriminate (including in our hiring and promotion practices) on the basis of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status or any other characteristic protected by law.\nOur internship pay is based on the position, your location, year in school, degree, and experience. For Poland: the annual range for our interns is 117,750 PLN - 204,100 PLN. 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