Applied Scientist 3

Oracle Oracle · Enterprise · Austin, TX +1

Applied Scientist role at Oracle focused on developing and deploying state-of-the-art machine learning algorithms and systems, including Generative AI and LLMs. Responsibilities include building prototypes, creating solutions for enterprise products, and contributing to production model code. The role involves research in areas like efficient neural networks, VLM, and reinforcement learning for AI alignment.

What you'd actually do

  1. Invent, implement and deploy state-of-the-art machine learning and/or specific domain industry algorithms and systems.
  2. Build prototypes and explore conceptually new solutions.
  3. Work collaboratively with science, engineering, and product teams to identify customer needs in order to create and implement solutions, promote innovation and drive model implementations.
  4. Applies data science capabilities and research findings to create and implement solutions to scale.
  5. Responsible for developing new intelligence around core products and services through applied research on behalf of our customers.

Skills

Required

  • Master’s degree in Computer Science, Engineering, or related technical field and 4 years of experience in the job offered or in a computer-related occupation.
  • Deep Learning, Generative AI, LLM
  • Python, C and C++
  • Dataset design and curation at scale, and Synthetic data generation
  • OpenCV, Git, and HuggingFace
  • PyTorch, TensorFlow, and Keras
  • Safety and Responsible AI evaluation and red teaming
  • LLM evaluation and benchmarking, LLM-as-a-Judge calibration and validation
  • Reinforcement Learning - DPO, PPO, KTO, and GRPO
  • VLM, Diffusion models, image generation, VLM as a judge evaluation
  • Datamining and statistical concepts
  • ML libraries, scikit-learn and Pandas
  • statistical models for Machine Learning solutions, regression, classification and clustering
  • Distributed systems
  • Production operations and troubleshooting
  • Technical proposals, design specs, architecture diagrams and presentations

What the JD emphasized

  • Deep Learning, Generative AI, LLM
  • Safety and Responsible AI evaluation and red teaming
  • LLM evaluation and benchmarking, LLM-as-a-Judge calibration and validation
  • Reinforcement Learning - DPO, PPO, KTO, and GRPO
  • VLM, Diffusion models, image generation, VLM as a judge evaluation

Other signals

  • Develops models, prototypes, and experiments that pave the way for innovative products and services.
  • Contributes to writing production model code.
  • Work with Software Engineering teams to deploy them in production.
  • Design and implement algorithms, train models, and deploy both to production to validate premises and achieve goals.