Appen
Appen
Appen कृत्रिम बुद्धिमत्ता के उत्पादों और अनुप्रयोगों को आधार देने वाली डेटा नींव विकसित करती है। 1996 में स्थापित यह कंपनी AI डेटा जीवनचक्र के विभिन्न चरणों में स्केलेबल सेवाएँ प्रदान करती है, जिनमें डेटा संग्रह, क्यूरेशन, मॉडल फाइन-ट्यूनिंग और निगरानी शामिल हैं। इसका कार्य मानवीय विशेषज्ञता और AI को मिलाकर उद्यमों को अपने डेटा का अधिक प्रभावी ढंग से उपयोग करने में सहायता करता है। कृत्रिम बुद्धिमत्ता, परामर्श और मार्केटिंग के क्षेत्र में अपने अनुभव के साथ Appen विभिन्न उद्योगों में AI तकनीकों का विकास और परिनियोजन करने वाले संगठनों का समर्थन करती है।

Applied AI Research Engineer — Appen, India (Remote)

Join Appen as an Applied AI Research Engineer developing reinforcement-learning environments, LLM pipelines, and evaluation systems for global AI training-data programs. Create reusable research assets for frontier laboratories and customer projects.

विवरण

  • Create reinforcement-learning and agent environments for customer and frontier-lab applications, covering task design, scoring, and evaluation
  • Develop benchmarks and evaluation harnesses that assess model and data quality across accuracy, robustness, safety, latency, and cost
  • Build LLM pipelines and agentic systems for research, evaluation, and customer trials
  • Conduct fine-tuning, adapter, and related model experiments to understand how data and methods affect model behavior
  • Deploy local and self-hosted models for evaluation, inference, and workflow automation
  • Record experiments, configurations, datasets, results, and known limitations to support reproducible work
  • Collaborate with the GenAI Research team and cross-functional partners to convert technical work into reusable customer-engagement assets

आवश्यकताएँ

  • Bachelor’s, master’s, or doctoral degree in computer science, engineering, machine learning, or a related technical discipline
  • At least three years of professional engineering or relevant industry experience in AI/ML or software engineering
  • Proven software engineering ability, including experience creating dependable and maintainable AI systems
  • Practical experience with agentic systems, reinforcement-learning environments, LLM pipelines, or comparable AI applications
  • Experience creating evaluation harnesses, benchmarks, or model-testing pipelines
  • Ability to independently tackle technical challenges and rapidly turn research questions or ideas into working solutions
  • Strong knowledge of experimentation, reproducibility, and technical documentation

लाभ

  • Flexible ways of working
  • Access to tools, resources, and development opportunities that support capability building
  • The chance to tackle complex AI problems across multiple industries and regions
  • A collaborative culture centered on innovation, accountability, curiosity, and excellence

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