Scale AI
Machine Learning Engineer, Public Sector
About this role
Scale is seeking a Machine Learning Engineer for its Public Sector team to deploy state-of-the-art generative AI, computer vision, and agentic AI models into mission-critical government systems. You'll work across LLMs and vision models, building production infrastructure and optimizing models for real-world defense applications.
What you'll do
- Deploy and maintain production ML models, including retraining, hyperparameter tuning, and architectural refinement
- Develop agentic LLM systems integrated with retrieval pipelines and APIs to solve complex operational challenges
- Build and optimize large-scale fine-tuning pipelines for computer vision and multimodal models
- Collaborate with product and research teams to prototype and ship ML-driven product enhancements
- Design scalable machine learning infrastructure to automate and optimize ML services
- Act as ML advocate across engineering and product, representing technical best practices
What they're looking for
- Generative AI and agentic AI systems
- Large language models and natural language processing
- Computer vision and deep learning
- Python programming
- TensorFlow or PyTorch
- Deep reinforcement learning
- Cloud platforms (AWS or GCP)
- ML evaluation frameworks and model deployment
Benefits
- Comprehensive health, dental, and vision coverage
- Equity-based compensation (subject to Board approval)
- Retirement benefits
- Learning and development stipend
- Generous PTO
- Commuter stipend eligibility
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Scale AI
Scale AI builds a Generative AI Data Engine and ML infrastructure platforms that power LLM training, evaluation, and production serving at scale, along with data solutions for robotics and autonomous driving. The company is hiring Software Engineers for full-stack feature development and infrastructure systems, identity/security specialists for platform engineering, and Solutions Engineers to support enterprise clients and pre-sales processes.
- Website
- scale.com
Likely interview questions
- Describe a production ML system you've built from scratch—what was your approach to evaluation and deployment?
- How have you approached fine-tuning large models for domain-specific tasks, and how did you measure success?