{"id":2741727,"url":"https://alion.io/job/marvell-technology-systems-performance-engineer-ai-solutions-lab","title":"Systems Performance Engineer, AI Solutions Lab","company":{"id":269,"name":"Marvell Technology","domain":"marvell.com","url":"https://alion.io/company/marvell-technology","size_band":"5000+","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Workday","truth_index":{"grade":"A","score":95,"open_postings":51,"ghost_share":0,"stale_share":0.176,"repost_share":0.02,"time_to_fill_p50_days":54,"computed_at":"2026-10-17T05:46:00Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":null,"employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Santa Clara, United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":154000,"max_usd":337000,"period":"year","method":"role_country_seniority_unknown","sample_n":4027},"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":true,"technologies":[{"name":"DeepSpeed","optional":false},{"name":"FSDP","optional":false},{"name":"HPC","optional":false},{"name":"KV Cache","optional":false},{"name":"Linux","optional":false},{"name":"Megatron-LM","optional":false},{"name":"NCCL","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"Quantization","optional":false},{"name":"ROCm","optional":false},{"name":"SGLang","optional":false},{"name":"TensorRT-LLM","optional":false},{"name":"vLLM","optional":false},{"name":"ChatGPT","optional":true},{"name":"Copilot","optional":true},{"name":"Docker","optional":true},{"name":"GPTQ","optional":true},{"name":"Kubernetes","optional":true},{"name":"TensorRT","optional":true}],"status":"live","first_seen_at":"2026-10-16T20:59:47Z","employer_posted_date":"2026-10-16","last_verified_at":"2026-10-17T19:23:36Z","board_verified":true,"closed_at":null,"days_open":1,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":1},"description":"About Marvell\nMarvell’s semiconductor solutions are the essential building blocks of the data infrastructure that connects our world. Across enterprise, cloud and AI, and carrier architectures, our innovative technology is enabling new possibilities.\nAt Marvell, you can affect the arc of individual lives, lift the trajectory of entire industries, and fuel the transformative potential of tomorrow. For those looking to make their mark on purposeful and enduring innovation, above and beyond fleeting trends, Marvell is a place to thrive, learn, and lead.\nYour Team, Your Impact\nThe Solutions Architecture team sits within Marvell's Platform organization, which turns advanced silicon into deployable, customer-ready AI and cloud infrastructure. This team - the AI Solutions Lab - owns the validation layer that proves our platforms perform as promised, across compute, networking, and memory fabrics. Without it, Marvell could design great silicon but could never prove, at rack scale, that it performs the way customers expect - and hyperscalers don't buy chips on paper, they buy validated systems.Day to day, the team runs AI benchmarking experiments that mirror real customer workloads, operates GPU/HPC lab clusters, validates Marvell's Teralynx switching and AI fabrics under production-like conditions, and builds live demos for trade shows - carrying platform programs from concept to production.\nWhat sets this team apart is breadth of ownership: engineers here aren't limited to validating a single switch chip - they work across the full breadth of Marvell's AI platform portfolio, from Teralynx network fabrics to integrated optical platforms like co-packaged optics (CPO) and near-packaged optics (NPO), as well as emerging memory fabric products such as CXL and photonic fabric memory appliances. This cross-domain ownership - networking, optics, and memory, together - is work that's often siloed across separate teams elsewhere, and the results feed directly into customer proof points and executive reviews.\nWhat You Can Expect\nDrive end-to-end performance optimization across AI serving and training stacks using leading models and customer-relevant configurations.\nIdentify bottlenecks across the full data path, from application request flow through compute, memory, communication layers, GPU kernels, network transport, and data/storage pipelines.\nDevelop and execute benchmark methodologies that are reproducible, technically credible, and relevant to customer use cases.\nProfile serving-stack internals - quantization, scheduling, KV-cache management, and disaggregated prefill/decode - to identify concrete optimization targets.\nAnalyze results and develop performance narratives that guide kernel, runtime, communication, and system-level optimizations.\nWork closely with NRE / SRE and network or fabric architects to close competitive gaps through targeted tuning.\nCreate performance summaries, visuals, and benchmark artifacts for customer presentations, executive reviews, and trade-show demonstrations.\nWhat We're Looking For\nRequired Qualifications\nExperience with AI serving frameworks such as vLLM, SGLang, TensorRT-LLM, or similar platforms.\nStrong background in RCCL/NCCL, RDMA, communication-compute overlap, and performance profiling.\nHands-on experience with any GPU profiling tools (e.g., Nsight Systems/Compute, PyTorch Profiler, or ROCm Compute Profiler) to localize bottlenecks at the kernel level.\nDemonstrated ability to diagnose end-to-end bottlenecks across application, framework, runtime, and infrastructure layers.\nWorking knowledge of at least one distributed training framework (PyTorch DDP/FSDP, DeepSpeed, or Megatron-LM), given the role's scope spans both training and inference stacks.\nStrong Python and Linux skills; Go experience is a plus.\nComfortable working in containerized environments (Docker; Kubernetes a plus) to ensure benchmark reproducibility.\nAbility to translate deep technical analysis into clear recommendations and concise performance narratives.\nPreferred Qualifications\nExperience benchmarking distributed large-model inference and training systems.\nFamiliarity with GPU kernel analysis, system tracing, and workload-specific optimization.\nFamiliarity with serving-stack internals such as continuous batching, paged KV-cache attention, and quantization techniques (e.g., GPTQ, SmoothQuant).\nExposure to customer proofs of concept, competitive benchmarking, or externally consumed performance reporting.\nEducation\nBachelor's or Master's degree in Computer Science, Computer Engineering, or a related field; a PhD is a plus.\nExpected Base Pay Range (USD)\n186,900 - 275,000, $ per annumThe successful candidate’s starting base pay will be determined based on job-related skills, experience, qualifications, work location and market conditions.The expected base pay range for this role may be modified based on market conditions.\n\nThis role is eligible to participate in Marvell's bonus and equity plans, including a new hire equity grant and annual equity refreshers, ensuring you are rewarded for your contributions and remain invested in the company's long-term success.\nAdditional Compensation and Benefit Elements\nMarvell is committed to providing exceptional, comprehensive benefits that support our employees at every stage - from internship to retirement and through life’s most important moments. Our offerings are built around four key pillars: financial well-being, family support, mental and physical health, and recognition. Highlights include an employee stock purchase plan with a 2-year look back, family support programs to help balance work and home life, robust mental health resources to prioritize emotional well-being, and a recognition and service awards to celebrate contributions and milestones. We look forward to sharing more with you during the interview process.All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, sexual orientation, gender identity, disability or protected veteran status.\nAny applicant who requires a reasonable accommodation during the selection process should contact Marvell HR Helpdesk at .\nInterview Integrity\nTo support fair and authentic hiring practices, candidates are not permitted to use AI tools (such as transcription apps, real-time answer generators like ChatGPT or Copilot, or automated note-taking bots) during interviews.\nThese tools must not be used to record, assist with, or enhance responses in any way. Our interviews are designed to evaluate your individual experience, thought process, and communication skills in real time. Use of AI tools without prior instruction from the interviewer will result in disqualification from the hiring process.\nThis position may require access to technology and/or software subject to U.S. export control laws and regulations, including the Export Administration Regulations (EAR). As such, applicants must be eligible to access export-controlled information as defined under applicable law. Marvell may be required to obtain export licensing approval from the U.S. Department of Commerce and/or the U.S. Department of State. Except for U.S. citizens, lawful permanent residents, or protected individuals as defined by 8 U.S.C. 1324b(a)(3), all applicants may be subject to an export license review process prior to employment.\n#LI-AO1","description_format":"text","description_chars":7410,"description_truncated":false,"requirements":{"experience_years_min":null,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":{"level":"bachelor","optional":false},"security_clearance":false,"languages":[]},"benefits":["Equity"],"hiring_locations":[],"hiring_excludes":[{"name":"United States","iso":"US"}],"relocation_offered":false,"industries":["Network Equipment","AI Chips & Accelerators","Semiconductor IP & Chip Design Services","Optics & Photonic Components"],"lifecycle":[{"event":"open","at":"2026-10-16T20:59:47Z"}],"visa":[],"liveness":{"score":90,"band":"hot","label":"Hiring now","p_open":1,"p_active":0.903,"p_room":1,"age_days":0,"expected_fill_days":54,"reasons":["conf:4","velocity","win:early","comp:brand"],"computed_at":"2026-10-17T05:46:00Z"},"pay":null,"html_url":"https://alion.io/job/marvell-technology-systems-performance-engineer-ai-solutions-lab","json_url":"https://alion.io/job/marvell-technology-systems-performance-engineer-ai-solutions-lab.json","meta":{"generated_at":"2026-10-18T00:51:27Z","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":1484,"day_limit":null,"remaining_today":null,"minute_limit":300,"resets_at":"2026-10-19T00:00:00Z"}},"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":269},"rest":"https://alion.io/mcp/rest/get_company?id=269"},"human":"https://alion.io/catalog?offer=company.slices&for=job%2Fmarvell-technology-systems-performance-engineer-ai-solutions-lab"},{"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%2Fmarvell-technology-systems-performance-engineer-ai-solutions-lab"},{"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%2Fmarvell-technology-systems-performance-engineer-ai-solutions-lab"},{"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/marvell-technology-systems-performance-engineer-ai-solutions-lab\", \"note\": \"what you need it for\"}"},"human":"https://alion.io/catalog?offer=company.verify&for=job%2Fmarvell-technology-systems-performance-engineer-ai-solutions-lab"}]}