Senior Machine Learning Engineer, Public Sector

Scale AI Scale AI · Data AI · Washington, DC · Public Sector Engineering

Senior Machine Learning Engineer focused on deploying and improving generative AI, computer vision, reinforcement learning, and agentic AI models for mission-critical government systems. The role involves building agent frameworks, fine-tuning models, and advancing research in RL for LLMs, with a strong emphasis on production environments and large datasets.

What you'd actually do

  1. Take state of the art models developed internally and from the community, use them in production to solve problems for our customers and taskers
  2. Improve and maintain production models through retraining, hyperparameter tuning, and architectural updates, while preserving core performance characteristics
  3. Collaborate with product and research teams to identify and prototype ML-driven product enhancements, including for upcoming product lines
  4. Work with massive datasets to develop both generic models as well as fine tune models for specific products
  5. Build scalable machine learning infrastructure to automate and optimize our ML services

Skills

Required

  • Python
  • Tensorflow or PyTorch
  • GenAI
  • Agentic AI
  • natural language processing
  • deep learning
  • deep reinforcement learning
  • computer vision
  • production environment

Nice to have

  • Graduate degree in Computer Science, Machine Learning or Artificial Intelligence specialization
  • cloud platforms (eg. AWS or GCP)
  • deploying machine learning models in cloud environments
  • computer vision
  • generative AI models
  • large language models
  • agentic systems
  • ML evaluation frameworks
  • agentic model design

What the JD emphasized

  • This role will require an active security clearance or the ability to obtain a security clearance
  • Extensive experience with GenAI, Agentic AI, natural language processing, deep learning and deep reinforcement learning, or computer vision in a production environment

Other signals

  • deploying cutting-edge models to mission-critical government systems
  • developing agentic systems that help solve complex operational and planning challenges
  • building agent frameworks that integrate with custom retrieval pipelines and production APIs
  • advancing research in areas like reinforcement learning for agentic LLMs
  • training advanced models to increase labeling throughput and automate perception tasks
  • building large-scale fine-tuning pipelines
  • training models across multiple modalities
  • developing generalizable vision foundation models