{"id":3185011,"url":"https://alion.io/job/nvidia-deep-learning-model-and-system-optimization-engineering-intern-2027","title":"Deep Learning Model and System Optimization Engineering Intern – 2027","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":805,"ghost_share":0.024,"stale_share":0.732,"repost_share":0.043,"time_to_fill_p50_days":35,"computed_at":"2026-10-25T05:49:26Z"}},"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":["Shanghai, China","Beijing, China"],"countries":["CN"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":21000,"max_usd":41000,"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":"C++","optional":false},{"name":"CUDA","optional":false},{"name":"KV Cache","optional":false},{"name":"LLM","optional":false},{"name":"Mixture of Experts","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":"Transformers","optional":false},{"name":"Triton","optional":false},{"name":"vLLM","optional":false},{"name":"TensorRT","optional":true}],"status":"live","first_seen_at":"2026-10-23T23:16:54Z","employer_posted_date":"2026-10-23","last_verified_at":"2026-10-24T08:48:21Z","board_verified":true,"closed_at":null,"days_open":1,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":1},"description":"We are now looking for a Deep Learning Performance Engineer Intern to join our team. Large language models are transforming how people interact with computing, while placing unprecedented demands on inference performance, memory efficiency, scalability, and deployment cost.\nOur team develops GPU-accelerated software for deep learning inference. We work across model architectures, algorithms, numerical representations, inference systems, and GPU architectures to continuously improve the performance and deployment capabilities of large language models on NVIDIA GPUs. Our work contributes to inference frameworks such as TensorRT-LLM and helps bring state-of-the-art models to production efficiently. We are looking for outstanding interns who are passionate about large language model inference, low-precision computing, GPU performance optimization, and hardware-software co-design.\nWhat you’ll be doing:\nAnalyze and optimize inference performance across model architecture, numerical precision, GPU kernels, memory systems, and end-to-end serving frameworks.\nDevelop low-bit quantization techniques for model weights, activations, attention operands, and KV caches.\nExplore numerical formats and mixed-precision strategies involving FP8, NVFP4, MXFP4, and other emerging low-precision formats while preserving model accuracy and capabilities.\nDesign and evaluate techniques for KV cache compression, sparsification, hierarchical storage, and reuse across inference requests.\nOptimize the inference of model architectures including MHA, GQA, MQA, MLA, sliding-window attention, sparse attention, linear attention, and state space models.\nInvestigate model-system co-optimization techniques such as speculative decoding, state reuse, and computation reuse.\nBuild performance models and optimization strategies for long-context inference and agentic workloads, with a focus on throughput, time to first token, inter-token latency, memory efficiency, and deployment cost.\nCollaborate closely with model, framework, kernel, library, system software, and GPU architecture teams to influence the design of future models, software platforms, and hardware architectures.\nWhat we need to see:\nPursuing an MS or PhD from a leading university in Computer Science, Computer Engineering, Electrical Engineering, Applied Mathematics, or a related field, with an expected graduation date in 2028.\nAvailable to work as an intern for at least three months and at least four days per week.\nStrong understanding of Transformer architectures and large language model inference, including core components such as attention, MoE, and KV cache management.\nExperience with at least one large language model inference framework, such as TensorRT-LLM, vLLM, SGLang, or a comparable framework.\nProficiency in Python and PyTorch, with strong experimental design, data analysis, debugging, and problem-solving skills.\nExperience with at least one GPU programming technology, such as CUDA C/C++, Triton, or a comparable parallel programming framework.\nUnderstanding of GPU architecture, memory hierarchy, Tensor Cores, parallel computing, and performance analysis.\nFamiliarity with low-precision computing concepts, including quantization error, scaling, rounding, clipping, group or block quantization, and mixed-precision computation.\nAbility to evaluate an optimization technique across model accuracy, kernel performance, and end-to-end inference performance.\nGood communication, technical writing, organization, and cross-functional collaboration skills.\nWays to stand out from the crowd:\nHands-on experience optimizing kernels or end-to-end workloads on NVIDIA GPUs.\nResearch or engineering experience in model quantization, KV cache optimization, efficient attention, speculative decoding, or long-context inference.\nContributions to inference frameworks, GPU libraries, compilers, or other relevant open-source projects.\nExperience profiling and optimizing production-scale LLM inference workloads.\nNVIDIA is committed to fostering a diverse work environment and is proud to be an equal opportunity employer. We highly value diversity among our current and future employees and do not discriminate in our hiring or employment 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.","description_format":"text","description_chars":4424,"description_truncated":false,"requirements":{"experience_years_min":null,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":{"level":"master","optional":false},"security_clearance":false,"languages":[]},"benefits":[],"hiring_locations":[],"hiring_excludes":[],"relocation_offered":false,"industries":["Processors, MCUs & AI Chips","Servers & Data Center Hardware","Computer Components","AI Chips & Accelerators"],"lifecycle":[{"event":"open","at":"2026-10-23T23:16:54Z"}],"visa":[],"liveness":{"score":63,"band":"ok","label":"Likely open","p_open":1,"p_active":0.632,"p_room":1,"age_days":1,"expected_fill_days":35,"reasons":["conf:21","stale_co","velocity","win:early","comp:junior,brand"],"computed_at":"2026-10-25T05:49:26Z"},"pay":null,"html_url":"https://alion.io/job/nvidia-deep-learning-model-and-system-optimization-engineering-intern-2027","json_url":"https://alion.io/job/nvidia-deep-learning-model-and-system-optimization-engineering-intern-2027.json","meta":{"generated_at":"2026-10-25T09:07:15Z","cache_seconds":300,"methodology":"https://alion.io/methodology","terms":"https://alion.io/terms","contact":"https://alion.io/contact","api":"https://alion.io/developers","about":"Alion is a live layer of people, companies and AI agents: who they are, whether they are real and active right now, what they do and how to work with them, readable by people and by agents and paid per call.","catalog":"https://alion.io/catalog.json","usage":{"tier":"crawler_verified","counted_by":"address","units_charged":1,"used_today":13035,"day_limit":null,"remaining_today":null,"minute_limit":300,"resets_at":"2026-10-26T00:00:00Z"}},"offers":[{"id":"company.slices","title":"One company in depth, by slice","status":"live","price":{"credits":0.02,"usd":0.002,"plus_per_slice":{"credits":0.05,"usd":0.005}},"unit":"per company, plus each slice with data","note":"the employer in depth","call":{"mcp_tool":"get_company","arguments":{"id":6},"rest":"https://alion.io/mcp/rest/get_company?id=6"},"human":"https://alion.io/catalog?offer=company.slices&for=job%2Fnvidia-deep-learning-model-and-system-optimization-engineering-intern-2027"},{"id":"market.stats","title":"A market slice: pay, demand and time to fill","status":"live","price":{"credits":1,"usd":0.1},"unit":"per slice","note":"pay, demand and time to fill for this role and place","call":{"mcp_tool":"market_stats"},"human":"https://alion.io/catalog?offer=market.stats&for=job%2Fnvidia-deep-learning-model-and-system-optimization-engineering-intern-2027"},{"id":"job.search","title":"Open jobs by role, technology, place, pay and visa","status":"live","price":{"credits":0.02,"usd":0.002},"unit":"per posting in a list","note":"similar open postings","call":{"mcp_tool":"search_jobs"},"human":"https://alion.io/catalog?offer=job.search&for=job%2Fnvidia-deep-learning-model-and-system-optimization-engineering-intern-2027"},{"id":"company.verify","title":"Is this company real and active right now","status":"pilot","price":null,"unit":"per company","request":{"url":"https://alion.io/catalog/request","method":"POST","body":"{\"offer\": \"company.verify\", \"for\": \"job/nvidia-deep-learning-model-and-system-optimization-engineering-intern-2027\", \"note\": \"what you need it for\"}"},"human":"https://alion.io/catalog?offer=company.verify&for=job%2Fnvidia-deep-learning-model-and-system-optimization-engineering-intern-2027"}]}