About Scale AI
Scale AI is the data foundation for AI, helping organizations build and deploy reliable production AI applications. We partner with leading enterprises and government organizations to accelerate their AI initiatives through our data annotation platform, generative AI solutions, and enterprise AI capabilities.
Role Overview
As a Forward Deployed AI Engineer on our Enterprise team, you'll be the technical bridge between Scale AI's cutting-edge AI capabilities and our most strategic customers. You'll work with enterprise clients to understand their unique challenges, architect custom AI solutions, and ensure successful deployment and adoption of AI systems in production environments.
This is a hands-on technical role that combines deep engineering expertise with customer-facing problem solving. You'll work directly with customer engineering teams to integrate AI into their critical workflows.
Key Responsibilities
Customer Integration & Deployment
- Partner directly with enterprise customers to understand their technical infrastructure, data pipelines, and business requirements
- Design and implement custom integrations between Scale AI's platform and customer data environments (cloud platforms, data warehouses, internal APIs)
- Build robust data connectors and ETL pipelines to ingest, process, and prepare customer data for AI workflows
- Deploy and configure AI models and agents within customer security and compliance boundaries
AI Agent Development
- Develop production-grade AI agents tailored to customer use cases across domains like customer support, data analysis, content generation, and workflow automation
- Architect multi-agent systems that orchestrate between different models, tools, and data sources
- Implement evaluation frameworks to measure agent performance and iterate toward business objectives
- Design human-in-the-loop workflows and feedback mechanisms for continuous agent improvement
Prompt Engineering & Optimization
- Create sophisticated prompt engineering strategies optimized for customer-specific domains and data
- Build and maintain prompt libraries, templates, and best practices for customer use cases
- Conduct systematic prompt experimentation and A/B testing to improve model outputs
- Implement RAG (Retrieval Augmented Generation) systems and fine-tuning pipelines where appropriate
Technical Leadership & Collaboration
- Serve as the primary technical point of contact for strategic enterprise accounts
- Collaborate with customer data scientists, ML engineers, and software developers to ensure smooth integration
- Provide technical training and knowledge transfer to customer teams
- Work closely with Scale's product and engineering teams to translate customer needs into product improvements
- Document technical architectures, integration patterns, and best practices
Problem Solving & Innovation
- Debug complex technical issues across the entire stack, from data pipelines to model outputs
- Rapidly prototype solutions to unblock customers and prove out new use cases
- Stay current on the latest AI/ML research and tools, bringing innovative approaches to customer problems
- Identify opportunities for productization based on common customer patterns
Required Qualifications
- 4+ years of software engineering experience with strong fundamentals in data structures, algorithms, and system design
- Production Python expertise with experience in modern ML/AI frameworks (e.g., LangChain, LlamaIndex, HuggingFace, OpenAI API)
- Experience with cloud platforms (AWS, GCP, or Azure) and modern data infrastructure
- Proven ability to work with customers in a technical consulting, solutions engineering, or product engineering role
- Strong problem-solving skills with the ability to navigate ambiguous requirements and rapidly iterate toward solutions
- Excellent communication skills with the ability to explain complex technical concepts to both technical and non-technical audiences
Preferred Qualifications
- Deep understanding of LLMs including prompting techniques, fine-tuning, embeddings, and RAG architectures
- Experience building and deploying AI agents or autonomous systems in production
- Background in machine learning, with hands-on experience training or fine-tuning models
- Familiarity with enterprise security, compliance, and governance requirements (SOC 2, GDPR, HIPAA)
- Experience with containerization (Docker, Kubernetes) and CI/CD pipelines
- Knowledge of vector databases and semantic search systems
- Previous work in a forward deployed, field engineering, or technical account management role
- Domain expertise in verticals like finance, healthcare, government, or manufacturing
- Contributions to open-source AI/ML projects
