NVIDIA
NVIDIA
NVIDIA गेमिंग, डेटा सेंटर, क्लाउड कंप्यूटिंग, स्वास्थ्य सेवा, विनिर्माण, ऑटोमोटिव और रोबोटिक्स सहित विभिन्न क्षेत्रों में उपयोग की जाने वाली त्वरित कंप्यूटिंग और कृत्रिम बुद्धिमत्ता प्रौद्योगिकियाँ विकसित करती है। इसका कार्य ग्राफ़िक्स प्रोसेसिंग यूनिट, AI प्लेटफ़ॉर्म, सिमुलेशन और उद्योग-केंद्रित समाधानों तक फैला हुआ है। इनमें सहयोगात्मक 3D वर्कफ़्लो के लिए NVIDIA Omniverse, स्वायत्त वाहन विकास के लिए NVIDIA DRIVE और स्वास्थ्य सेवा अनुप्रयोगों के लिए NVIDIA Clara शामिल हैं। कंपनी उन्नत कंप्यूटिंग, सिमुलेशन और AI-आधारित विश्लेषणों को समर्थन देने वाले बुनियादी ढाँचे और सॉफ़्टवेयर पर काम करने वाली बड़ी तकनीकी टीमों को एक साथ लाती है।

Senior Architect, Coding Model Performance and Post-Training at NVIDIA (Remote, US)

Lead performance evaluation and post-training for NVIDIA’s AI models for code generation and software engineering. Develop benchmarks, reward strategies, and scalable training infrastructure.

विवरण

  • Create frameworks to evaluate coding models across code generation, debugging, completion, repository reasoning, and multi-step software engineering tasks.
  • Develop and optimize post-training pipelines using supervised fine-tuning, preference optimization, reinforcement learning, and outcome- or process-based reward modeling.
  • Analyze benchmark results, training dynamics, failure modes, and model behavior to identify improvement opportunities.
  • Improve performance on complex coding tasks through targeted training, reward design, curriculum development, and iterative error analysis.
  • Build scalable, reproducible infrastructure for training experiments, evaluation pipelines, and performance analysis.
  • Work with research scientists, infrastructure engineers, and product teams to turn model insights into production capabilities.

आवश्यकताएँ

  • Bachelor’s degree or equivalent experience in computer science or AI.
  • At least 12 years of relevant work experience.
  • Strong Python programming skills.
  • Strong knowledge of large language models, transformer architectures, and generative model training and evaluation.
  • Foundational knowledge of reinforcement learning algorithms and implementation, including MDPs, policies, and reward design.
  • Deep knowledge of software engineering fundamentals, code generation, automated testing, and program correctness.
  • A record of leading model development lifecycles, including dataset versioning, experiment tracking, and evaluation pipelines.
  • Experience training or post-training state-of-the-art coding models or models for software engineering agents.
  • Experience with execution-based and verifiable rewards, reward hacking mitigation, and automated code evaluation.
  • Familiarity with coding benchmarks and evaluation environments such as SWE-bench.

लाभ

  • Competitive salary.
  • Comprehensive benefits package.
  • Equity.
  • Benefits for employees and their families.

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