Applied Scientist Ii, Alexa International

Amazon Amazon · Big Tech · IN, KA, Bengaluru · Applied Science

Applied Scientist II at Amazon Alexa International focusing on developing and applying LLMs and multimodal systems for multi-lingual applications. The role involves research, fine-tuning/post-training LLMs, building evaluation metrics, and driving scientific strategy from research to production, impacting global customers.

What you'd actually do

  1. Build novel online & offline evaluation metrics and methodologies for multimodal personal digital assistants.
  2. Fine-tune/post-train LLMs using advanced and innovative techniques like SFT, DPO, Reinforcement Learning (RLHF and RLAIF) for supporting model performance specific to a customer’s location and language.
  3. Contribute through industry-first research to drive innovation forward.
  4. Drive cross-team scientific strategy and influence partner teams on LLM evaluation frameworks, post-training methodologies, and best practices for international speech and language systems.
  5. Lead end-to-end delivery of scientifically complex solutions from research to production, including reusable science components and services that resolve architecture deficiencies across teams.

Skills

Required

  • deep learning
  • generative models
  • LLMs
  • multimodal systems
  • large-scale data
  • scientific research
  • applied AI
  • multi-lingual applications
  • text
  • speech
  • vision domains
  • LLM evaluation & tooling
  • LLM architectures
  • SFT
  • DPO
  • Reinforcement Learning (RLHF and RLAIF)
  • model performance
  • experimentation framework
  • A/B testing
  • industry-first research
  • scientific strategy
  • post-training methodologies
  • best practices for international speech and language systems
  • end-to-end delivery
  • research to production
  • reusable science components
  • scientific thought leader
  • publications
  • community engagement
  • Java
  • C++
  • Python
  • algorithms and data structures
  • parsing
  • numerical optimization
  • data mining
  • parallel and distributed computing
  • high-performance computing

Nice to have

  • Unix/Linux
  • professional software development

What the JD emphasized

  • novel algorithms and modeling techniques
  • industry-leading scientific research and applied AI
  • multi-lingual applications
  • LLM evaluation & tooling
  • pushing boundaries
  • swiftly delivering impactful solutions
  • novel online & offline evaluation metrics and methodologies
  • Fine-tune/post-train LLMs
  • advanced and innovative techniques
  • Reinforcement Learning (RLHF and RLAIF)
  • experimentation framework
  • agile model and data analysis
  • industry-first research
  • scientific strategy
  • LLM evaluation frameworks
  • post-training methodologies
  • best practices for international speech and language systems
  • end-to-end delivery
  • scientifically complex solutions
  • research to production
  • reusable science components
  • architecture deficiencies
  • scientific thought leader
  • publications

Other signals

  • LLMs
  • multimodal systems
  • deep learning
  • generative models
  • large-scale data
  • scientific research
  • applied AI
  • multi-lingual applications
  • text
  • speech
  • vision domains
  • LLM evaluation & tooling
  • LLM architectures
  • SFT
  • DPO
  • Reinforcement Learning (RLHF and RLAIF)
  • model performance
  • experimentation framework
  • A/B testing
  • industry-first research
  • scientific strategy
  • post-training methodologies
  • best practices for international speech and language systems
  • end-to-end delivery
  • research to production
  • reusable science components
  • scientific thought leader
  • publications
  • community engagement