{"id":2910042,"url":"https://alion.io/job/amd-ml-systems-research-engineer-rl-inference-agent-systems","title":"ML Systems Research Engineer, RL / Inference / Agent Systems","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":"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":174000,"max_usd":384000,"period":"year","method":"role_country_seniority_unknown","sample_n":4194},"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AI Agents","optional":false},{"name":"Function Calling","optional":false},{"name":"LLM","optional":false},{"name":"Post-training","optional":false},{"name":"Reinforcement Learning","optional":false},{"name":"Reward Modeling","optional":false},{"name":"CUDA","optional":true},{"name":"GRPO","optional":true},{"name":"JAX","optional":true},{"name":"Kubernetes","optional":true},{"name":"Machine Learning","optional":true},{"name":"Python","optional":true},{"name":"PyTorch","optional":true},{"name":"Ray","optional":true},{"name":"RLHF","optional":true},{"name":"ROCm","optional":true},{"name":"SLURM","optional":true},{"name":"TensorFlow","optional":true}],"status":"live","first_seen_at":"2026-10-12T16:36:00Z","employer_posted_date":"2026-10-20","last_verified_at":"2026-10-25T13:31:37Z","board_verified":true,"closed_at":null,"days_open":13,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":13},"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:\nWe are hiring ML Systems Research Engineers to build the reinforcement learning, inference, and evaluation infrastructure behind AI-for-engineering systems. This role focuses on the systems that let agents and models improve real engineering workflows: running many attempts, evaluating correctness, measuring performance, managing long-latency rewards, and feeding results back into model and agent improvement.\nYou will work across compute optimization, hardware engineering automation, verification, simulation, debugging. The emphasis is on scalable ML systems that make research practical, repeatable, and useful for production engineering teams.\nTHE PERSON:\nYou are a systems-minded ML engineer or researcher who understands that model quality depends on the surrounding loop: data, tools, inference, graders, reward design, logging, and iteration speed. You can build reliable infrastructure, reason about RL and inference tradeoffs, and collaborate with scientists and applied engineers to make experiments reproducible and useful.\nKEY RESPONSIBILITIES\nBuild RL and inference systems for agentic engineering workflows, including job orchestration, sampling, scoring, caching, experiment tracking, and reproducible evaluation.\nDevelop infrastructure for long-horizon and high-latency reward tasks where validation can take minutes to hours.\nDesign staged rewards, proxy graders, sliced evaluation paths, retry strategies, and uncertainty-aware evaluation methods.\nSupport optimization workflows with systems for candidate generation, benchmark execution, correctness checking, profiler feedback, reward modeling, and model-level improvement.\nPartner with AI research scientists on reward hacking research, reward shaping, metareasoning, and post-training methods for engineering tasks.\nBuild scalable inference and tool-use pipelines for LLM agents that interact with compilers, profilers, simulators, formal tools, benchmark harnesses, and internal knowledge sources.\nStandardize datasets, eval definitions, run logs, leaderboards, failure taxonomies, and data collection for future training.\nAnalyze experimental results and turn system behavior into actionable guidance for model, agent, tool, and reward improvements.\nTECHNICAL FOCUS AREAS\nReinforcement learning and post-training infrastructure for tool-using agents.\nInference systems for LLMs and agents, including latency, throughput, batching, sampling, reliability, and observability.\nEvaluation systems for tasks with expensive, delayed, mixed, or sparse rewards.\nReward design for engineering domains where correctness, performance, quality, and resource usage must be balanced.\nDistributed experimentation, job orchestration, caching, data pipelines, dashboards, and reproducible run management.\nIntegration with external tools such as compilers, profilers, simulators, validation systems, benchmark harnesses, and ticketing or knowledge systems.\nPREFERRED QUALIFICATIONS\nStrong programming skills in Python and experience with ML frameworks such as PyTorch, JAX, TensorFlow, or similar.\nExperience building ML systems, RL infrastructure, inference services, agent frameworks, evaluation platforms, or distributed experimentation systems.\nStrong understanding of model inference, batching, sampling, latency, throughput, observability, and reliability tradeoffs.\nAbility to design experiments and evaluation pipelines with clear metrics, logs, reproducibility, and statistical discipline.\n1Strong collaboration skills with AI researchers, applied engineers, infrastructure engineers, and hardware domain experts.\nPREFERRED EXPERIENCE\nExperience with reinforcement learning, RLHF, GRPO, preference optimization, reward modeling, reward shaping, or post-training systems.\nExperience with LLM agents, tool-use systems, code generation, automated program repair, compiler optimization, or benchmark-driven development.\nExperience with distributed systems, job orchestration, Kubernetes, Ray, Slurm, workflow engines, data pipelines, or large-scale experiment management.\nFamiliarity with GPU systems, ROCm/HIP, CUDA, profiling, kernel benchmarking, model serving, or distributed training/inference.\nExposure to hardware engineering workflows such as design, verification, firmware, simulation, or performance analysis is a strong plus.\nPublications or shipped systems in ML systems, RL, inference optimization, AI infrastructure, or hardware/software co-design are valued.\nEDUCATION\nBachelor's degree in Computer Science, Computer Engineering, Electrical Engineering, Machine Learning, or related field, or equivalent practical experience. Master's preferred; PhD is a plus, especially with work in ML systems, reinforcement learning, distributed systems, GPU computing, or AI infrastructure.\nLOCATION: Santa Clara, CA\n#LI-BW1\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:\nWe are hiring ML Systems Research Engineers to build the reinforcement learning, inference, and evaluation infrastructure behind AI-for-engineering systems. This role focuses on the systems that let agents and models improve real engineering workflows: running many attempts, evaluating correctness, measuring performance, managing long-latency rewards, and feeding results back into model and agent improvement.\nYou will work across compute optimization, hardware engineering automation, verification, simulation, debugging. The emphasis is on scalable ML systems that make research practical, repeatable, and useful for production engineering teams.\nTHE PERSON:\nYou are a systems-minded ML engineer or researcher who understands that model quality depends on the surrounding loop: data, tools, inference, graders, reward design, logging, and iteration speed. You can build reliable infrastructure, reason about RL and inference tradeoffs, and collaborate with scientists and applied engineers to make experiments reproducible and useful.\nKEY RESPONSIBILITIES\nBuild RL and inference systems for agentic engineering workflows, including job orchestration, sampling, scoring, caching, experiment tracking, and reproducible evaluation.\nDevelop infrastructure for long-horizon and high-latency reward tasks where validation can take minutes to hours.\nDesign staged rewards, proxy graders, sliced evaluation paths, retry strategies, and uncertainty-aware evaluation methods.\nSupport optimization workflows with systems for candidate generation, benchmark execution, correctness checking, profiler feedback, reward modeling, and model-level improvement.\nPartner with AI research scientists on reward hacking research, reward shaping, metareasoning, and post-training methods for engineering tasks.\nBuild scalable inference and tool-use pipelines for LLM agents that interact with compilers, profilers, simulators, formal tools, benchmark harnesses, and internal knowledge sources.\nStandardize datasets, eval definitions, run logs, leaderboards, failure taxonomies, and data collection for future training.\nAnalyze experimental results and turn system behavior into actionable guidance for model, agent, tool, and reward improvements.\nTECHNICAL FOCUS AREAS\nReinforcement learning and post-training infrastructure for tool-using agents.\nInference systems for LLMs and agents, including latency, throughput, batching, sampling, reliability, and observability.\nEvaluation systems for tasks with expensive, delayed, mixed, or sparse rewards.\nReward design for engineering domains where correctness, performance, quality, and resource usage must be balanced.\nDistributed experimentation, job orchestration, caching, data pipelines, dashboards, and reproducible run management.\nIntegration with external tools such as compilers, profilers, simulators, validation systems, benchmark harnesses, and ticketing or knowledge systems.\nPREFERRED QUALIFICATIONS\nStrong programming skills in Python and experience with ML frameworks such as PyTorch, JAX, TensorFlow, or similar.\nExperience building ML systems, RL infrastructure, inference services, agent frameworks, evaluation platforms, or distributed experimentation systems.\nStrong understanding of model inference, batching, sampling, latency, throughput, observability, and reliability tradeoffs.\nAbility to design experiments and evaluation pipelines with clear metrics, logs, reproducibility, and statistical discipline.\n1Strong collaboration skills with AI researchers, applied engineers, infrastructure engineers, and hardware domain experts.\nPREFERRED EXPERIENCE\nExperience with reinforcement learning, RLHF, GRPO, preference optimization, reward modeling, reward shaping, or post-training systems.\nExperience with LLM agents, tool-use systems, code generation, automated program repair, compiler optimization, or benchmark-driven development.\nExperience with distributed systems, job orchestration, Kubernetes, Ray, Slurm, workflow engines, data pipelines, or large-scale experiment management.\nFamiliarity with GPU systems, ROCm/HIP, CUDA, profiling, kernel benchmarking, model serving, or distributed training/inference.\nExposure to hardware engineering workflows such as design, verification, firmware, simulation, or performance analysis is a strong plus.\nPublications or shipped systems in ML systems, RL, inference optimization, AI infrastructure, or hardware/software co-design are valued.\nEDUCATION\nBachelor's degree in Computer Science, Computer Engineering, Electrical Engineering, Machine Learning, or related field, or equivalent practical experience. Master's preferred; PhD is a plus, especially with work in ML systems, reinforcement learning, distributed systems, GPU computing, or AI infrastructure.\nLOCATION: Santa Clara, CA\n#LI-BW1\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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