{"id":3424076,"url":"https://alion.io/job/amd-sr-staff-distributed-systems-software-engineer-collectives","title":"Sr. Staff Distributed Systems Software Engineer, Collectives","company":{"id":93,"name":"AMD","domain":"amd.com","url":"https://alion.io/company/amd","size_band":"1001-5000","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Jibe","truth_index":{"grade":"A","score":97,"open_postings":17,"ghost_share":0,"stale_share":0.118,"repost_share":0,"time_to_fill_p50_days":49,"computed_at":"2026-11-08T05:47:12Z"}},"role":"Backend","role_family":"Backend","seniority":"staff","employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Austin, United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":179000,"max_usd":334000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":1500},"experience_years_min":null,"visa_sponsorship":true,"relocation_package":false,"has_equity":false,"technologies":[{"name":"C++","optional":false},{"name":"Linux","optional":false},{"name":"LLM","optional":false},{"name":"Multimodal AI","optional":false},{"name":"ROCm","optional":false},{"name":"JAX","optional":true},{"name":"NCCL","optional":true},{"name":"Python","optional":true},{"name":"PyTorch","optional":true},{"name":"SGLang","optional":true},{"name":"vLLM","optional":true}],"status":"live","first_seen_at":"2026-10-28T18:13:00Z","employer_posted_date":"2026-10-28","last_verified_at":"2026-11-06T09:08:37Z","board_verified":false,"closed_at":null,"days_open":10,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":10},"description":"ADVANCE YOUR CAREER. ADVANCE THE WORLD.\nAt AMD, we believe technology has the power to solve the world’s most important challenges. From advancing healthcare and scientific discovery to powering AI and the technologies people rely on every day, innovation at AMD is shaping the future.\nWhether you’re designing next-gen processors, enabling AI breakthroughs, or bringing leading edge products to market, every role at AMD contributes to something bigger - technology that moves the world forward. Join us and, together, we’ll advance your career.\nTHE ROLE:\nAs an experienced member of the Collectives and Network Performance team, you will play an important role in optimizing collective communication and networking software for AMD GPUs. You will enhance communication libraries and training/inference performance across multi-GPU and multi-node systems, working with internal GPU library, runtime and ROCm teams and with open-source maintainers to integrate optimizations across multiple generations of AMD GPUs and a range of state-of-the-art AI models.\nTHE PERSON:\nSeeking an experienced C++ developer with strong technical and analytical skills in Linux environments. The ideal candidate can take ownership of features and projects with limited guidance, contribute to defining scope and technical approach, and collaborate effectively within a high-performing team. You have solid knowledge of network, NIC and GPU hardware architecture, hands-on experience profiling and optimizing distributed AI workloads, and a good understanding of modern training and inference optimization. You also support the growth of less experienced engineers.\nKEY RESPONSIBILITIES:\nDevelop Collective Communication Features: Design, implement and optimize features in collective communication libraries across scale-up and scale-out networks.\nOptimize Distributed Training & Inference: Profile, tune and analyze LLM, diffusion, multimodal, RecSys and generative AI workloads on single-node and multi-node systems, and recommend design tradeoffs.\nOwn Features End to End: Drive assigned features and projects from design through delivery with limited guidance, including scoping, implementation, testing and performance validation.\nPerformance Modeling & Tooling: Develop and improve tools and infrastructure for benchmarking, performance estimation, regression tracking and reporting.\nCollaborate with Framework & Library Teams: Work with internal GPU library, runtime and ROCm teams, and with open-source maintainers, to ensure code changes meet requirements and are integrated upstream.\nHardware-Software Co-Design Input: Contribute software performance data and analysis to discussions on future scale-up networks, NICs and scale-out networks.\nCustomer & Stakeholder Support: Help customers and internal teams apply effective load balancing, workload scheduling and model sharding strategies, and present results and recommendations.\nSoftware Engineering Best Practices: Apply sound engineering principles to deliver robust, maintainable solutions.\nGuide & Mentor: Mentor junior engineers through code reviews, knowledge sharing and technical guidance.\nPREFERRED EXPERIENCE:\n Collective Communication & Network Knowledge: Strong knowledge of collective communication libraries (e.g., RCCL, NCCL, MPI) and a good understanding of NIC, RDMA and scale-up and scale-out network architectures.\nPerformance Analysis: Strong experience in profiling, performance analysis and bottleneck identification for distributed AI workloads.\nDeep Learning Integration: Hands-on experience integrating optimized compute or communication into ML frameworks such as PyTorch, JAX, vLLM or SGLang, with a focus on throughput and scalability.\nSoftware Engineering Excellence: Strong proficiency in C++ and Python with solid experience in performance tuning, debugging and test design.\nHigh-Performance Computing: Experience with large-scale, multi-node compute environments and optimizing AI workloads across clusters.\nCollaboration & Communication: Ability to work effectively with cross-functional teams, mentor less experienced engineers, and communicate results clearly.\nIndustry Experience: senior level of professional experience in technical software development, with a focus on performance engineering, communication libraries, networking, or GPU/distributed systems.\nACADEMIC CREDENTIALS:\nMaster’s degree in Computer Science, Computer Engineering, Electrical Engineering, or a related field (or a Bachelor’s degree with equivalent experience). A PhD is a plus.\nLOCATION:\nAustin, Texas or San Jose, California.\nThis role is not eligible for visa sponsorship.\n#LI-MV1\nBenefits offered are described: AMD benefits at a glance.\nAMD does not accept unsolicited resumes from headhunters, recruitment agencies, or fee-based recruitment services. AMD and its subsidiaries are equal opportunity, inclusive employers and will consider all applicants without regard to age, ancestry, color, marital status, medical condition, mental or physical disability, national origin, race, religion, political and/or third-party affiliation, sex, pregnancy, sexual orientation, gender identity, military or veteran status, or any other characteristic protected by law. We encourage applications from all qualified candidates and will accommodate applicants’ needs under the respective laws throughout all stages of the recruitment and selection process.\nAMD may use Artificial Intelligence to help screen, assess or select applicants for this position. AMD’s “Responsible AI Policy” is available here.\n\nThis posting is for an existing vacancy.\nTHE ROLE:\nAs an experienced member of the Collectives and Network Performance team, you will play an important role in optimizing collective communication and networking software for AMD GPUs. You will enhance communication libraries and training/inference performance across multi-GPU and multi-node systems, working with internal GPU library, runtime and ROCm teams and with open-source maintainers to integrate optimizations across multiple generations of AMD GPUs and a range of state-of-the-art AI models.\nTHE PERSON:\nSeeking an experienced C++ developer with strong technical and analytical skills in Linux environments. The ideal candidate can take ownership of features and projects with limited guidance, contribute to defining scope and technical approach, and collaborate effectively within a high-performing team. You have solid knowledge of network, NIC and GPU hardware architecture, hands-on experience profiling and optimizing distributed AI workloads, and a good understanding of modern training and inference optimization. You also support the growth of less experienced engineers.\nKEY RESPONSIBILITIES:\nDevelop Collective Communication Features: Design, implement and optimize features in collective communication libraries across scale-up and scale-out networks.\nOptimize Distributed Training & Inference: Profile, tune and analyze LLM, diffusion, multimodal, RecSys and generative AI workloads on single-node and multi-node systems, and recommend design tradeoffs.\nOwn Features End to End: Drive assigned features and projects from design through delivery with limited guidance, including scoping, implementation, testing and performance validation.\nPerformance Modeling & Tooling: Develop and improve tools and infrastructure for benchmarking, performance estimation, regression tracking and reporting.\nCollaborate with Framework & Library Teams: Work with internal GPU library, runtime and ROCm teams, and with open-source maintainers, to ensure code changes meet requirements and are integrated upstream.\nHardware-Software Co-Design Input: Contribute software performance data and analysis to discussions on future scale-up networks, NICs and scale-out networks.\nCustomer & Stakeholder Support: Help customers and internal teams apply effective load balancing, workload scheduling and model sharding strategies, and present results and recommendations.\nSoftware Engineering Best Practices: Apply sound engineering principles to deliver robust, maintainable solutions.\nGuide & Mentor: Mentor junior engineers through code reviews, knowledge sharing and technical guidance.\nPREFERRED EXPERIENCE:\n Collective Communication & Network Knowledge: Strong knowledge of collective communication libraries (e.g., RCCL, NCCL, MPI) and a good understanding of NIC, RDMA and scale-up and scale-out network architectures.\nPerformance Analysis: Strong experience in profiling, performance analysis and bottleneck identification for distributed AI workloads.\nDeep Learning Integration: Hands-on experience integrating optimized compute or communication into ML frameworks such as PyTorch, JAX, vLLM or SGLang, with a focus on throughput and scalability.\nSoftware Engineering Excellence: Strong proficiency in C++ and Python with solid experience in performance tuning, debugging and test design.\nHigh-Performance Computing: Experience with large-scale, multi-node compute environments and optimizing AI workloads across clusters.\nCollaboration & Communication: Ability to work effectively with cross-functional teams, mentor less experienced engineers, and communicate results clearly.\nIndustry Experience: senior level of professional experience in technical software development, with a focus on performance engineering, communication libraries, networking, or GPU/distributed systems.\nACADEMIC CREDENTIALS:\nMaster’s degree in Computer Science, Computer Engineering, Electrical Engineering, or a related field (or a Bachelor’s degree with equivalent experience). A PhD is a plus.\nLOCATION:\nAustin, Texas or San Jose, California.\nThis role is not eligible for visa sponsorship.\n#LI-MV1\nBenefits offered are described: AMD benefits at a glance.\nAMD does not accept unsolicited resumes from headhunters, recruitment agencies, or fee-based recruitment services. AMD and its subsidiaries are equal opportunity, inclusive employers and will consider all applicants without regard to age, ancestry, color, marital status, medical condition, mental or physical disability, national origin, race, religion, political and/or third-party affiliation, sex, pregnancy, sexual orientation, gender identity, military or veteran status, or any other characteristic protected by law. We encourage applications from all qualified candidates and will accommodate applicants’ needs under the respective laws throughout all stages of the recruitment and selection process.\nAMD may use Artificial Intelligence to help screen, assess or select applicants for this position. AMD’s “Responsible AI Policy” is available here.\n\nThis posting is for an existing vacancy.","description_format":"text","description_chars":10650,"description_truncated":false,"requirements":{"experience_years_min":null,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":{"level":"bachelor","optional":true},"security_clearance":false,"languages":[]},"benefits":[],"hiring_locations":[],"hiring_excludes":[],"relocation_offered":false,"industries":["Processors, MCUs & AI Chips","Computer Components","AI Chips & Accelerators"],"lifecycle":[{"event":"open","at":"2026-10-28T21:46:35Z"}],"visa":[],"liveness":{"score":90,"band":"hot","label":"Hiring now","p_open":1,"p_active":0.903,"p_room":1,"age_days":10,"expected_fill_days":49,"reasons":["conf:44","velocity","win:early","comp:brand"],"computed_at":"2026-11-08T05:47:12Z"},"pay":null,"html_url":"https://alion.io/job/amd-sr-staff-distributed-systems-software-engineer-collectives","json_url":"https://alion.io/job/amd-sr-staff-distributed-systems-software-engineer-collectives.json","meta":{"generated_at":"2026-11-08T10:54:24Z","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":23442,"day_limit":null,"remaining_today":null,"minute_limit":300,"resets_at":"2026-11-09T00:00:00Z"}},"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":93},"rest":"https://alion.io/mcp/rest/get_company?id=93"},"human":"https://alion.io/catalog?offer=company.slices&for=job%2Famd-sr-staff-distributed-systems-software-engineer-collectives"},{"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%2Famd-sr-staff-distributed-systems-software-engineer-collectives"},{"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%2Famd-sr-staff-distributed-systems-software-engineer-collectives"},{"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/amd-sr-staff-distributed-systems-software-engineer-collectives\", \"note\": \"what you need it for\"}"},"human":"https://alion.io/catalog?offer=company.verify&for=job%2Famd-sr-staff-distributed-systems-software-engineer-collectives"}],"agent":{"mcp":"https://alion.io/mcp/jobs","mcp_open":"https://alion.io/mcp/open","next":[{"tool":"get_job","arguments":{"id":3424076}},{"tool":"search_jobs","arguments":{"company":"amd.com","detail":"compact"}},{"tool":"match_people","arguments":{"job_id":3424076},"note":"people open to offers who fit this vacancy"}],"note":"The same data through MCP tools: https://alion.io/mcp/open needs no sign-in (compact searches, postings, people); https://alion.io/mcp with an Alion account has everything. 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