Senior AI Agent Engineer

Saga•Los Altos, CA

9d•Remote

About The Position

Saga is building the infrastructure and products for the next generation of AI agents. Our AI Character Agent Network allows studios, creators, and publishers to turn their most iconic IP into living, breathing digital companions that engage fans across social media, drive organic user acquisition, and power a new kind of agentic commerce. Our agents run on a proprietary training architecture that combines LLMs and SLMs to produce fine-tuned character AI agents — autonomous enough to develop their own intelligence and personality, while guided by the creative vision of the original studios and creators. We've processed over 23 million transactions worth more than $2 billion since our early 2024 launch, and we're just getting started. Our team is world-class — veteran web3 entrepreneurs and builders from Apple, X (formerly Twitter), Robinhood, Samsung, NVIDIA, Tendermint and Skuchain — and we're looking to grow as we scale our agent platform to more studios, more platforms, and more users. We offer a flexible, remote working environment. You can expect a warm welcome from a friendly and international team that will support you in your personal and professional growth. The Role As a Senior AI Agent Engineer, you'll be at the center of how we build, train, deploy, and operate character AI agents at scale. You'll work across the full lifecycle — from fine-tuning models and orchestrating SLM swarms to deploying agents across social platforms and building the infrastructure that keeps them running reliably. This role blends ML engineering, backend development, and systems thinking.

Requirements

5+ years of backend or ML engineering experience, with hands-on work deploying AI/ML models to production
Experience with LLM fine-tuning, prompt engineering, and inference optimization
Strong proficiency in Python; experience with Golang is a plus
Experience building and operating API services and data pipelines at scale
Familiarity with reinforcement learning techniques (RLHF, reward modeling) or agent framework development
Understanding of multi-model architectures — orchestrating multiple models or agents to collaborate on tasks
Experience deploying applications that integrate with third-party platforms and APIs (social media, messaging, commerce)
Strong systems thinking — comfortable reasoning about distributed systems, scaling, and reliability
Exceptional problem-solving and communication skills

Nice To Haves

Experience with character AI, conversational AI, or NPC behavior systems
Background in gaming, entertainment, or media technology
Experience with multi-modal AI (voice, image, video generation or processing)
Familiarity with blockchain infrastructure, on-chain payments, or crypto-native commerce
Experience with MCP (Model Context Protocol) or agent-to-agent communication protocols
Understanding of content safety, trust & safety systems, or responsible AI practices
Contributions to open-source AI/ML projects

Responsibilities

Build and maintain the training and inference pipelines for character AI agents, including LLM/SLM orchestration and swarm-based architectures
Deploy and operate AI agents across social media platforms (Instagram, X, WhatsApp, TikTok) with consistent character behavior and personality coherence
Develop tooling for studios and creators to customize agent personality, lore, brand guidelines, and behavioral guardrails
Build and improve the feedback loops — fine-tuning, reward models, RLHF, and RLAIF — that keep agents improving over time
Architect and scale the infrastructure supporting agent deployments, including multi-modal capabilities (text, voice, video, livestreaming)
Contribute to the agentic commerce platform — enabling agents to drive transactions, recommend products, and interact with payment systems
Implement safety and content moderation systems to ensure agents behave appropriately across diverse user interactions
Collaborate with studio partners to translate creative direction into agent training parameters and behavioral specifications
Monitor agent performance, engagement metrics, and behavioral drift in production