{"id":2909824,"url":"https://alion.io/job/amd-forward-deployed-engineer-fde-for-ai-and-genai-inference-optimization","title":"Forward Deployed Engineer (FDE) for AI and GenAI Inference Optimization","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":"B","score":75,"open_postings":162,"ghost_share":0,"stale_share":0.994,"repost_share":0,"time_to_fill_p50_days":29,"computed_at":"2026-10-25T05:49:26Z"}},"role":"Solutions","role_family":"Solutions","seniority":"senior","employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Riyadh, Saudi Arabia"],"countries":["SA"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":65000,"max_usd":150000,"period":"year","method":"global_role_cell_scaled_by_country","sample_n":1760},"experience_years_min":8,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Agentic Workflows","optional":false},{"name":"AI Agents","optional":false},{"name":"C++","optional":false},{"name":"Claude","optional":false},{"name":"Copilot","optional":false},{"name":"CUDA","optional":false},{"name":"Cursor","optional":false},{"name":"KV Cache","optional":false},{"name":"LLM","optional":false},{"name":"Mixture of Experts","optional":false},{"name":"Multimodal AI","optional":false},{"name":"Python","optional":false},{"name":"Quantization","optional":false},{"name":"ROCm","optional":false},{"name":"SGLang","optional":false},{"name":"Speculative Decoding","optional":false},{"name":"Triton","optional":false},{"name":"vLLM","optional":false},{"name":"DeepSeek","optional":true},{"name":"MiniMax","optional":true},{"name":"Qwen","optional":true}],"status":"live","first_seen_at":"2026-10-20T08:53:00Z","employer_posted_date":"2026-10-20","last_verified_at":"2026-10-25T17:12:45Z","board_verified":true,"closed_at":null,"days_open":5,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":5},"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.\nForward Deployed Engineer (FDE) for AI and GenAI Inference Optimization\nAbout the Role\nWe are seeking a Member of Technical Staff to join our Forward Deployed Engineering (FDE) team. In this customer-facing role, you will work directly with strategic partners and customers as part of the FDE team to enable, optimize, and deploy state-of-the-art GenAI inference workloads on AMD data center CPUs and GPUs (Instinct™ family). You will bridge the gap between cutting-edge AI models and AMD hardware, driving competitive performance across the full inference stack.\nResponsibilities\nPartner with customers as part of the FDE team to enable and optimize large language models (LLMs) and multimodal models on AMD Instinct GPUs, meeting committed performance and accuracy targets.\nOptimize GenAI inference pipelines on vLLM and SGLang, including PD disaggregation, expert parallelism (EP/wide-EP), tensor/data parallelism, speculative decoding, and KV cache management.\nDevelop and tune low-level GPU kernels (Triton, HIP, composable kernels) for attention, MoE, GEMM, quantization, and normalization operations.\nProfile end-to-end workloads, identify bottlenecks across GPU/CPU boundaries, and deliver measurable throughput and latency improvements versus competitive targets.\nEnable advanced data types and techniques (FP8, MXFP4, sparse attention) with a focus on both performance and numerical accuracy.\nDebug complex runtime, stability, and accuracy issues in production environments; partner with core engineering teams to identify root causes and contribute fixes.\nContribute optimizations back to open-source frameworks (vLLM, SGLang, ATOM, AITER) and internal libraries; document best practices and deliver production-ready artifacts to customers.\nLeverage AI-assisted coding tools and agentic workflows to accelerate development, debugging, optimization, and code review across the inference stack.\nWhat You'll Bring\nBS, MS, or PhD in Computer Science, Electrical Engineering, or a related field, along with 8+ years of relevant experience.\nSolid expertise in GPU architecture and performance optimization, including hands-on experience with AMD ROCm (or CUDA) and GPU kernel development.\nStrong proficiency in Python and C++, with experience developing and optimizing kernels using Triton, HIP, or CUDA.\n Experience with AI-assisted coding and agentic development tools (e.g., GitHub Copilot, Claude, Cursor, or equivalent) to enhance productivity in kernel development, large-scale codebase navigation, debugging, and framework integration.\nExperience working with LLM inference frameworks (vLLM, SGLang, or equivalent) and serving optimization techniques.\nSolid understanding of transformer and MoE architectures, quantization, and distributed inference strategies (TP, DP, EP, and related parallelism techniques).\nProficiency with profiling and debugging tools (e.g., rocProfiler, ROCdebug-agent).\nNice to Have\nExperience optimizing frontier models (DeepSeek, Qwen, Kimi, MiniMax, GLM, etc.) in production environments.\nTrack record of contributing to open-source AI inference frameworks.\nExperience integrating AI-driven automation into engineering workflows and developer tooling.\nStrong customer-facing communication skills and the ability to manage technical deliverables against committed timelines.\nAMD is committed to building a diverse and inclusive workplace. We encourage candidates to apply even if their experience does not align perfectly with every qualification listed. If you are excited about this role and believe you can make an impact, we would welcome your application.\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 availablehere.\n\nThis posting is for an existing vacancy.\nForward Deployed Engineer (FDE) for AI and GenAI Inference Optimization\nAbout the Role\nWe are seeking a Member of Technical Staff to join our Forward Deployed Engineering (FDE) team. In this customer-facing role, you will work directly with strategic partners and customers as part of the FDE team to enable, optimize, and deploy state-of-the-art GenAI inference workloads on AMD data center CPUs and GPUs (Instinct™ family). You will bridge the gap between cutting-edge AI models and AMD hardware, driving competitive performance across the full inference stack.\nResponsibilities\nPartner with customers as part of the FDE team to enable and optimize large language models (LLMs) and multimodal models on AMD Instinct GPUs, meeting committed performance and accuracy targets.\nOptimize GenAI inference pipelines on vLLM and SGLang, including PD disaggregation, expert parallelism (EP/wide-EP), tensor/data parallelism, speculative decoding, and KV cache management.\nDevelop and tune low-level GPU kernels (Triton, HIP, composable kernels) for attention, MoE, GEMM, quantization, and normalization operations.\nProfile end-to-end workloads, identify bottlenecks across GPU/CPU boundaries, and deliver measurable throughput and latency improvements versus competitive targets.\nEnable advanced data types and techniques (FP8, MXFP4, sparse attention) with a focus on both performance and numerical accuracy.\nDebug complex runtime, stability, and accuracy issues in production environments; partner with core engineering teams to identify root causes and contribute fixes.\nContribute optimizations back to open-source frameworks (vLLM, SGLang, ATOM, AITER) and internal libraries; document best practices and deliver production-ready artifacts to customers.\nLeverage AI-assisted coding tools and agentic workflows to accelerate development, debugging, optimization, and code review across the inference stack.\nWhat You'll Bring\nBS, MS, or PhD in Computer Science, Electrical Engineering, or a related field, along with 8+ years of relevant experience.\nSolid expertise in GPU architecture and performance optimization, including hands-on experience with AMD ROCm (or CUDA) and GPU kernel development.\nStrong proficiency in Python and C++, with experience developing and optimizing kernels using Triton, HIP, or CUDA.\n Experience with AI-assisted coding and agentic development tools (e.g., GitHub Copilot, Claude, Cursor, or equivalent) to enhance productivity in kernel development, large-scale codebase navigation, debugging, and framework integration.\nExperience working with LLM inference frameworks (vLLM, SGLang, or equivalent) and serving optimization techniques.\nSolid understanding of transformer and MoE architectures, quantization, and distributed inference strategies (TP, DP, EP, and related parallelism techniques).\nProficiency with profiling and debugging tools (e.g., rocProfiler, ROCdebug-agent).\nNice to Have\nExperience optimizing frontier models (DeepSeek, Qwen, Kimi, MiniMax, GLM, etc.) in production environments.\nTrack record of contributing to open-source AI inference frameworks.\nExperience integrating AI-driven automation into engineering workflows and developer tooling.\nStrong customer-facing communication skills and the ability to manage technical deliverables against committed timelines.\nAMD is committed to building a diverse and inclusive workplace. We encourage candidates to apply even if their experience does not align perfectly with every qualification listed. If you are excited about this role and believe you can make an impact, we would welcome your application.\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. 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