Frontier Agent Engineering Manager, Enterprise

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 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.

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

Leadership & Collaboration

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

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

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

Preferred Qualifications

Agent Development Wiz

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

Infrastructure Guru

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)

Customer Product Whisperer

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

Sample Projects

The following are some examples of the types of projects we’ve worked on with customers. All of these projects leverage customer data, integrate directly into customers’ existing systems, and are deployed on their infrastructure.

Deep Research for Due Diligence

For a global professional services firm, we developed a sophisticated deep research agent to assist in due diligence. This agent employs a multi-agent architecture for robust fact-checking, integrates several internal MCP tools, and processes complex, unstructured data sources. This solution reliably saves employees hundreds of hours weekly.

Churn Prediction

Working with a TelCo organization, we built a model utilizing customer data to predict churn likelihood. The system then curates personalized offers based on this prediction. This model was integrated into a "next best action" copilot, enabling call center agents to proactively surface relevant offers to customers, leading to a significant reduction in churn.

Data Extraction Voice Agent

We partnered with a healthcare organization to create a lifelike voice agent and avatar designed to gather unstructured health information from patients. Engineered for low latency, the agent adeptly manages conversational flow, adheres to safety guardrails, and efficiently handles data extraction. This automation saves the organization's nurses hundreds of hours each week.