{"id":63544,"url":"https://alion.io/job/nvidia-applied-research-scientist-calibration-decoding","title":"Applied Research Scientist, Calibration & Decoding","company":{"id":6,"name":"NVIDIA","domain":"nvidia.com","url":"https://alion.io/company/nvidia","size_band":"5000+","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Workday","truth_index":{"grade":"B","score":82,"open_postings":592,"ghost_share":0.019,"stale_share":0.66,"repost_share":0.034,"time_to_fill_p50_days":33,"computed_at":"2026-10-17T05:46:00Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":null,"employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Redmond, United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":{"min":60000,"max":150000,"currency":"USD","period":"year","gross":null,"usd_annual":150000},"salary_estimate":null,"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"CUDA Toolkit","optional":false},{"name":"Data Augmentation","optional":false},{"name":"Fine-tuning","optional":false},{"name":"LoRA","optional":false},{"name":"Machine Learning","optional":false},{"name":"OpenAI","optional":false},{"name":"PEFT","optional":false},{"name":"QLoRA","optional":false},{"name":"Reinforcement Learning","optional":false},{"name":"Sim-to-Real","optional":false},{"name":"Synthetic Data","optional":false}],"status":"live","first_seen_at":"2026-08-19T10:24:02Z","employer_posted_date":null,"last_verified_at":"2026-08-19T10:24:02Z","board_verified":false,"closed_at":null,"days_open":59,"trust":{"level":"not_scored","repost_count":null,"flags":[],"days_open":59},"description":"Job Description\nJob Title: Senior Quantum Applied Research Scientist, Calibration and Decoding. Location:\nRedmond, Washington, USA\nSanta Clara, California, USA\nRemote, California, USA\nResponsibilities:\nResearch and develop open AI models for quantum system calibration.\nBuild physics-informed synthetic data generation pipelines using quantum device models, noise channels, and Hamiltonian characterization.\nDevelop surrogate models of quantum hardware that capture device physics and drift behavior.\nBuild real-time AI systems that jointly account for calibration state and decoding requirements.\nCo-design model latency, throughput, and update cadence for fault-tolerant feedback loops;\nApply reinforcement learning and online learning to optimize calibration policies.\nDevelop GPU-accelerated implementations so the complete pipeline can scale.\nCommunicate research findings and collaborate with academic and industry partners.\nContribute to rapid innovation, technical depth, and creative problem solving.\nQualifications:\nMaster's degree in Physics, Computer Science, Electrical Engineering, Applied Mathematics, or a related field. A PhD is strongly preferred, or equivalent experience.\n8+ years of combined experience and high-impact work in quantum systems and AI/ML research.\nHands-on expertise in machine learning and deep learning for science or physics, including model architecture design, large-scale training, fine-tuning, and evaluation.\nStrong background in quantum device physics and quantum information science;\nUnderstanding of noise models, error mechanisms, and fault-tolerant quantum systems across one or more qubit modalities.\nBroad understanding of quantum control, including pulse-level hardware interfaces and classical feedback through software abstractions.\nExcellent communication and collaboration skills.\nPreferred:\nExperience developing learned calibration or decoding models and deploying them in real-time quantum control feedback loops.\nUnderstanding of latency and throughput constraints in real-time quantum systems.\nDeep expertise in reinforcement learning, including policy optimization, reward shaping, and sim-to-real transfer for physical systems or closed-loop control.\nExperience with physics-informed or generative approaches to synthetic data generation;\nExperience with noise simulation, Hamiltonian learning, or data augmentation for scientific AI models.\nExperience with large-scale model training and fine-tuning, including LoRA, QLoRA, adapters, and domain adaptation.\nProficiency with CUDA and NVIDIA GPU programming for quantum simulation, AI model training, or real-time inference at scale.\nBenefits and Compensation:\nBase salary range of $192,000 to $304,750 USD, depending on location, experience, and comparable employee compensation.\nEligible for equity and benefits.\nCompetitive salary and comprehensive benefits package.\nBenefits available to employees and their families.","description_format":"text","description_chars":2933,"description_truncated":false,"requirements":{"experience_years_min":null,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":{"level":"master","optional":true},"security_clearance":false,"languages":[]},"benefits":["Equity"],"hiring_locations":[],"hiring_excludes":[],"relocation_offered":false,"industries":["Processors, MCUs & AI Chips","Servers & Data Center Hardware","Computer Components","AI Chips & Accelerators"],"lifecycle":[{"event":"open","at":"2026-08-19T10:24:02Z"}],"visa":[{"country":"GB","licensed_sponsor":true,"evidence":"Licensed UK visa sponsor (Senior or Specialist Worker, Skilled Worker)","filings_12m":null,"filings_prev_12m":null,"green_card_filings_12m":null,"median_offered_wage_usd":null,"route":"Global Business Mobility: Senior or Specialist Worker; 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