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Scale AI is seeking a Frontier Agent Engineering Manager for the Enterprise team based in London. This is a management role combining deep engineering and AI expertise with customer-facing technical leadership.
You will serve as the technical bridge between Scale AI's AI capabilities and strategic enterprise customers. You'll lead a team that architects, deploys, and optimizes AI solutions in production environments, working directly with customer engineering teams to integrate AI into critical workflows.
Key Responsibilities:
Customer Integration & Deployment: Partner 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 environments (cloud platforms, data warehouses, internal APIs). Build robust data connectors and ETL pipelines to ingest 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 (customer support, data analysis, content generation, workflow automation). Architect multi-agent systems orchestrating 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 improvement.
Prompt Engineering & Optimization: Create sophisticated prompt engineering strategies optimized for customer-specific domains. Build and maintain prompt libraries, templates, and best practices. Conduct systematic prompt experimentation and A/B testing. Implement RAG systems and fine-tuning pipelines where appropriate.
Leadership & Collaboration: Serve as the Engineering Manager and technical point of contact for strategic enterprise accounts. Lead a team collaborating with customer data scientists, ML engineers, and software developers. Work with Scale's product and engineering teams to translate customer needs into product improvements. Document technical architectures and integration patterns.
Problem Solving & Innovation: Debug complex technical issues across the entire stack. Rapidly prototype solutions to unblock customers. Stay current on AI/ML research and tools. Identify opportunities for productization based on common customer patterns.
Sample projects include: developing a sophisticated deep research agent for due diligence at a professional services firm; building a churn prediction model integrated into a copilot for a telecom organization; and creating a voice agent for health data extraction at a healthcare organization.
Requirements:
- 5+ years of software engineering experience with 3+ years of management experience, with strong fundamentals in data structures, algorithms, and system design
- Production Python expertise with experience in modern ML/AI frameworks (LangChain, LlamaIndex, HuggingFace, OpenAI API)
- Experience with cloud platforms (AWS, GCP, or Azure) and modern data infrastructure
- Strong problem-solving skills with ability to navigate ambiguous requirements and rapidly iterate
- Excellent communication skills to explain complex technical concepts to technical and non-technical audiences
Preferred Qualifications:
- Deep understanding of LLMs including prompting techniques, embeddings, and RAG architectures
- Experience building and deploying AI agents or autonomous systems in production
- Knowledge of vector databases and semantic search systems
- Contributions to open-source AI/ML projects
- Experience with containerization (Docker, Kubernetes) and CI/CD pipelines
- Experience with Infrastructure as Code tools (Terraform, Bicep)
- Previous work in DevOps, platform, or infrastructure roles
- Familiarity with enterprise security, compliance, and governance (SOC 2, GDPR, HIPAA)
- Proven ability to work with customers in technical consulting, solutions engineering, or product engineering roles
- Domain expertise in finance, healthcare, government, or manufacturing
- Experience with technical enablement or teaching programs
