{"id":2911322,"url":"https://alion.io/job/amd-ai-systems-engineer-ai-model-training-inference","title":"AI Systems Engineer – AI Model (Training & Inference)","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":null},"role":"AI/ML","role_family":"AI/ML","seniority":"senior","employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"explicit","locations":["Markham, Canada"],"countries":["CA"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":95000,"max_usd":166000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":65},"experience_years_min":null,"visa_sponsorship":true,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Anomaly Detection","optional":false},{"name":"AWQ","optional":false},{"name":"DeepSpeed","optional":false},{"name":"FSDP","optional":false},{"name":"GPTQ","optional":false},{"name":"HPC","optional":false},{"name":"Kubernetes","optional":false},{"name":"KV Cache","optional":false},{"name":"Megatron-LM","optional":false},{"name":"MLIR","optional":false},{"name":"Quantization","optional":false},{"name":"SGLang","optional":false},{"name":"Speculative Decoding","optional":false},{"name":"TorchServe","optional":false},{"name":"Triton","optional":false},{"name":"vLLM","optional":false},{"name":"GPT-4","optional":true},{"name":"PyTorch","optional":true}],"status":"live","first_seen_at":"2026-09-10T14:31:00Z","employer_posted_date":"2026-10-20","last_verified_at":"2026-10-22T04:24:27Z","board_verified":true,"closed_at":null,"days_open":41,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":41},"description":"ADVANCE YOUR CAREER. ADVANCE THE WORLD.\nAt AMD, we believe technology can change lives for the better. It can heal us, entertain us, and make us more connected, productive, and understanding of the world around us. And we’re looking for talent who feel the same: people who want to leave the planet better than they found it, those who don’t shy away from humanity’s challenges but are determined to help solve them.\nAMD is powering the next generation of supercomputing, high-performance computing, cloud, and AI. Whether you’re designing next-gen processors, enabling AI breakthroughs, or creating go-to-market plans, every role at AMD contributes to something bigger - technology that moves the world forward.\n THE ROLE:\nThe AMD AI Group is looking for a Senior Software Development Engineer to own the end-to-end model execution stack on AMD Instinct GPUs - spanning training infrastructure at scale and high-performance inference serving.\nTHE PERSON:\nThis role demands someone who has shipped LLMs on real hardware, written GPU kernels that moved production metrics, and built the systems infrastructure (orchestration, storage, monitoring) that keeps thousands of GPUs productive. You will be instrumental in ensuring AMD GPUs are first-class citizens for frontier model training and inference across current and next-generation Instinct accelerators.\nKEY RESPONSIBILITIES:\nTraining Infrastructure & Enablement\nEnable and optimize large-scale model training (LLMs, VLMs, MoEarchitectures) on AMD Instinct GPU clusters, ensuring correctness, reproducibility, and competitive throughput.\nBuild and maintain training infrastructure: job orchestration, distributed checkpointing, data loading pipelines, and storage optimization for multi-thousand GPU clusters on Kubernetes.\nDebug and resolve training-specific issues including gradient norm explosions, non-deterministic behavior across GPU generations, and compute-communication overlap in distributed training (FSDP, DeepSpeed, Megatron-LM).\nOptimize RCCL collective communication patterns for training workloads, including all-reduce, all-gather, and reduce-scatter across multi-node topologies.\nDevelop monitoring, alerting, and compliance infrastructure to ensure training cluster health, data security, and SLA adherence at scale.\nDesign and build end-to-end validation and testing infrastructure using proxy workloads, synthetic benchmarks, and configurable workload generators to systematically validate platform readiness across AMD Instinct GPU generations.\nInference Optimization & Serving\nWrite and optimize high-performance GPU kernels (GEMM, attention, quantized matmul, GPTQ/AWQ) in HIP, Triton, and MLIR targeting AMD Instinct architectures, with demonstrated ability to outperform open-source baselines.\nDrive end-to-end inference enablement on new AMD GPU silicon - be among the first to get frontier models running on each new Instinct generation, creating reproducible guides and reference implementations.\nOptimize inference serving frameworks (vLLM, SGLang, TorchServe) for AMD GPUs: batching strategies, KV-cache management, speculative decoding, and continuous batching for production throughput/latency targets.\nDevelop novel approaches to inference acceleration, including bio-inspired algorithms, SLM-assisted batching, and custom scheduling strategies that exploit AMD hardware characteristics.\nBuild quantization pipelines (FP8, FP6, FP4, GPTQ, AWQ) for production model deployment, ensuring quality-performance tradeoffs are well-characterized across AMD GPU generations.\nCross-Cutting\nCollaborate with AMD silicon architecture and pre-silicon teams to provide software feedback and validate software stack integration on next-generation Instinct GPU designs for both training and inference workloads.\nBuild observability and automated analysis tooling: log analysis pipelines, anomaly detection, performance baselining, regression detection, and diagnostic workflows for large-scale GPU clusters.\nContribute to the open ROCmecosystem and AMD's developer experience - SDKs, CI dashboards, documentation, and developer cloud enablement.\nPREFERRED EXPERIENCE:\nIndustry experience shipping productionAI/ML infrastructure, with hands-on work spanning both training and inference. \nDirect experience enabling frontier models (GPT-4 class) on AMD Instinct hardware end-to-end.\nBackground in building anomaly detection, log analysis, or observability systems for large-scale distributed GPU infrastructure.\nFamiliarity with AMD Instinct MI-series architectures (MI300X, MI350X, MI355X) and RCCL communication library.\nContributions to open-source AI frameworks (PyTorch, vLLM, SGLang, DeepSpeed, Megatron-LM).\nExperience designing validation frameworks, proxy benchmarks, or synthetic workload suites for GPU infrastructure at scale.\nExperience with pre-silicon software validation or hardware-softwareco-verification workflows.\nPublications or patents in HPC, ML systems, or GPU kernel optimization.\nPREFERRED ACADEMIC CREDENTIALS:\nBachelor’s or Master’sdegree or Ph.D in Computer/Software Engineering, Computer Science, or related technical discipline\nLOCATION: Markham, Canada\nThis role is not eligible for visa sponsorship.\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:\nThe AMD AI Group is looking for a Senior Software Development Engineer to own the end-to-end model execution stack on AMD Instinct GPUs - spanning training infrastructure at scale and high-performance inference serving.\nTHE PERSON:\nThis role demands someone who has shipped LLMs on real hardware, written GPU kernels that moved production metrics, and built the systems infrastructure (orchestration, storage, monitoring) that keeps thousands of GPUs productive. You will be instrumental in ensuring AMD GPUs are first-class citizens for frontier model training and inference across current and next-generation Instinct accelerators.\nKEY RESPONSIBILITIES:\nTraining Infrastructure & Enablement\nEnable and optimize large-scale model training (LLMs, VLMs, MoEarchitectures) on AMD Instinct GPU clusters, ensuring correctness, reproducibility, and competitive throughput.\nBuild and maintain training infrastructure: job orchestration, distributed checkpointing, data loading pipelines, and storage optimization for multi-thousand GPU clusters on Kubernetes.\nDebug and resolve training-specific issues including gradient norm explosions, non-deterministic behavior across GPU generations, and compute-communication overlap in distributed training (FSDP, DeepSpeed, Megatron-LM).\nOptimize RCCL collective communication patterns for training workloads, including all-reduce, all-gather, and reduce-scatter across multi-node topologies.\nDevelop monitoring, alerting, and compliance infrastructure to ensure training cluster health, data security, and SLA adherence at scale.\nDesign and build end-to-end validation and testing infrastructure using proxy workloads, synthetic benchmarks, and configurable workload generators to systematically validate platform readiness across AMD Instinct GPU generations.\nInference Optimization & Serving\nWrite and optimize high-performance GPU kernels (GEMM, attention, quantized matmul, GPTQ/AWQ) in HIP, Triton, and MLIR targeting AMD Instinct architectures, with demonstrated ability to outperform open-source baselines.\nDrive end-to-end inference enablement on new AMD GPU silicon - be among the first to get frontier models running on each new Instinct generation, creating reproducible guides and reference implementations.\nOptimize inference serving frameworks (vLLM, SGLang, TorchServe) for AMD GPUs: batching strategies, KV-cache management, speculative decoding, and continuous batching for production throughput/latency targets.\nDevelop novel approaches to inference acceleration, including bio-inspired algorithms, SLM-assisted batching, and custom scheduling strategies that exploit AMD hardware characteristics.\nBuild quantization pipelines (FP8, FP6, FP4, GPTQ, AWQ) for production model deployment, ensuring quality-performance tradeoffs are well-characterized across AMD GPU generations.\nCross-Cutting\nCollaborate with AMD silicon architecture and pre-silicon teams to provide software feedback and validate software stack integration on next-generation Instinct GPU designs for both training and inference workloads.\nBuild observability and automated analysis tooling: log analysis pipelines, anomaly detection, performance baselining, regression detection, and diagnostic workflows for large-scale GPU clusters.\nContribute to the open ROCmecosystem and AMD's developer experience - SDKs, CI dashboards, documentation, and developer cloud enablement.\nPREFERRED EXPERIENCE:\nIndustry experience shipping productionAI/ML infrastructure, with hands-on work spanning both training and inference. \nDirect experience enabling frontier models (GPT-4 class) on AMD Instinct hardware end-to-end.\nBackground in building anomaly detection, log analysis, or observability systems for large-scale distributed GPU infrastructure.\nFamiliarity with AMD Instinct MI-series architectures (MI300X, MI350X, MI355X) and RCCL communication library.\nContributions to open-source AI frameworks (PyTorch, vLLM, SGLang, DeepSpeed, Megatron-LM).\nExperience designing validation frameworks, proxy benchmarks, or synthetic workload suites for GPU infrastructure at scale.\nExperience with pre-silicon software validation or hardware-softwareco-verification workflows.\nPublications or patents in HPC, ML systems, or GPU kernel optimization.\nPREFERRED ACADEMIC CREDENTIALS:\nBachelor’s or Master’sdegree or Ph.D in Computer/Software Engineering, Computer Science, or related technical discipline\nLOCATION: Markham, Canada\nThis role is not eligible for visa sponsorship.\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":11609,"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":[{"name":"Canada","iso":"CA","kind":"country"}],"hiring_excludes":[],"relocation_offered":false,"industries":["Artificial Intelligence","Processors, MCUs & AI Chips","Computer Components","AI Chips & Accelerators"],"lifecycle":[{"event":"open","at":"2026-10-20T09:36:36Z"}],"visa":[],"liveness":{"score":16,"band":"cold","label":"Long shot","p_open":1,"p_active":0.567,"p_room":0.28,"age_days":40,"expected_fill_days":15,"reasons":["conf:3","velocity","win:tail","crowd:brand"],"computed_at":"2026-10-21T05:46:15Z"},"pay":null,"html_url":"https://alion.io/job/amd-ai-systems-engineer-ai-model-training-inference","json_url":"https://alion.io/job/amd-ai-systems-engineer-ai-model-training-inference.json","meta":{"generated_at":"2026-10-22T04:59:31Z","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":5671,"day_limit":null,"remaining_today":null,"minute_limit":300,"resets_at":"2026-10-23T00:00:00Z"}},"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-ai-systems-engineer-ai-model-training-inference"},{"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-ai-systems-engineer-ai-model-training-inference"},{"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-ai-systems-engineer-ai-model-training-inference"},{"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-ai-systems-engineer-ai-model-training-inference\", \"note\": \"what you need it for\"}"},"human":"https://alion.io/catalog?offer=company.verify&for=job%2Famd-ai-systems-engineer-ai-model-training-inference"}]}