Frontier Agent Engineering Manager, Enterprise

Scale AI•San Francisco, CA

3h•$252,000 - $315,000•Remote

About The Position

As a Forward Deployed AI Engineering Manager 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, lead a team that architects specific AI solutions, and ensure successful deployment and adoption of AI systems in production environments. This is a Management role that combines deep engineering and AI expertise, leading a team, and working on customer-facing problems. You'll work directly with customer engineering teams to integrate AI into their critical workflows.

Requirements

5+ years of software engineering experience with 3+ yrs of Management 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
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

Nice To Haves

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 using Terraform, Bicep, or other Infrastructure as Code (IaC) tools
Previous work in a devops, platform, or infra role
Familiarity with enterprise security, compliance, and governance requirements (SOC 2, GDPR, HIPAA)
Proven ability to work with customers in a technical consulting, solutions engineering, or product engineering role
Domain expertise in verticals like finance, healthcare, government, or manufacturing
Experience with technical enablement or teaching programs

Responsibilities

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
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
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
Serve as the Engineering Manager and technical point of contact for strategic enterprise accounts
Lead a team that is collaborating with customer data scientists, ML engineers, and software developers to ensure smooth integration
Work closely with Scale's product and engineering teams to translate customer needs into product improvements
Document technical architectures, integration patterns, and best practices
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