Senior Machine Learning Engineer - Model Evaluations, Public Sector at Scale. Location: Washington, DC, United States. The goal is to leverage techniques in generative AI, computer vision, reinforcement learning, and agentic AI to improve Scale's products and customer experiences in production environments. Our Public Sector ML team deploys cutting-edge models to mission-critical government systems through products such as Donovan and Thunderforge, spanning multiple modalities with a strong focus on large language models and computer vision. In the LLM domain, we are developing agentic systems that help solve complex operational and planning challenges for government partners, including building agent frameworks that integrate with custom retrieval pipelines and production APIs, as well as evaluation tools to benchmark and refine agent behavior. We are advancing research in reinforcement learning for agentic LLMs, with successful deployment in real-world environments. On the computer vision front, we train advanced models to increase labeling throughput, automate perception tasks, and develop generalizable vision foundation models to support defense applications. You will: take state-of-the-art models developed internally and from the community and deploy them in production to solve problems for our customers; improve and maintain production models through retraining, hyperparameter tuning, and architectural updates, while preserving core performance characteristics; collaborate with product and research teams to identify and prototype ML-driven product enhancements, includ

Senior Machine Learning Engineer - Model Evaluations, Public Sector at Scale. Location: Washington, DC, United States. The goal is to leverage techniques in generative AI, computer vision, reinforcement learning, and agentic AI to improve Scale's products and customer experiences in production environments. Our Public Sector ML team deploys cutting-edge models to mission-critical government systems through products such as Donovan and Thunderforge, spanning multiple modalities with a strong focus on large language models and computer vision. In the LLM domain, we are developing agentic systems that help solve complex operational and planning challenges for government partners, including building agent frameworks that integrate with custom retrieval pipelines and production APIs, as well as evaluation tools to benchmark and refine agent behavior. We are advancing research in reinforcement learning for agentic LLMs, with successful deployment in real-world environments. On the computer vision front, we train advanced models to increase labeling throughput, automate perception tasks, and develop generalizable vision foundation models to support defense applications. You will: take state-of-the-art models developed internally and from the community and deploy them in production to solve problems for our customers; improve and maintain production models through retraining, hyperparameter tuning, and architectural updates, while preserving core performance characteristics; collaborate with product and research teams to identify and prototype ML-driven product enhancements, includ