Senior Data Scientist

Boeing · Aerospace · Arlington, VA +1

Senior Data Scientist at Boeing specializing in Generative AI (GenAI), focusing on developing and optimizing advanced GenAI models, including LLMs and multi-modal systems. Responsibilities include fine-tuning, evaluation, prompt engineering, prototyping applications, assessing bias/fairness, collaborating with MLOps for scaling inference, and mentoring junior scientists.

What you'd actually do

  1. Lead the development and deployment of advanced GenAI models, including LLMs and multi-modal systems.
  2. Design and implement robust pipelines for model fine-tuning and evaluation.
  3. Develop and evaluate prompt engineering strategies and embedding techniques.
  4. Prototype and productionize GenAI applications that solve complex business problems.
  5. Own model performance evaluation and bias/fairness assessments to ensure ethical deployment.

Skills

Required

  • Bachelor’s degree in computer science, Machine Learning, Applied Mathematics, Computer Engineering, Software Engineering, Artificial Intelligence, Physics or a closely related field.
  • 5+ years of experience in deep learning frameworks
  • 1+ year of experience fine-tuning open-source LLMs and integrating APIs from commercial providers.
  • 5+ years of programming experience in Python
  • experience with data engineering workflows (e.g., Spark, Airflow, SQL)

Nice to have

  • Master's or PhD in Computer Science, Machine Learning, Applied Mathematics, Computer Engineering, Software Engineering, Artificial Intelligence, Physics or a closely related field
  • Experience fine-tuning open-source models and integrating APIs from commercial providers.
  • Experience with deep learning frameworks (e.g., PyTorch, TensorFlow).
  • Experience in AI ethics and governance in generative models.
  • Experience with data engineering tools (e.g., SQL, Spark

What the JD emphasized

  • export control compliance requirements
  • U.S. Person

Other signals

  • Generative AI
  • LLMs
  • multi-modal systems
  • fine-tuning
  • evaluation
  • prompt engineering
  • embedding techniques
  • productionize GenAI applications
  • model performance evaluation
  • bias/fairness assessments
  • ethical deployment
  • MLOps
  • model inference
  • monitor performance
  • GenAI strategy
  • industry trends
  • mentor junior scientists