Software Engineer, AI Platform (Senior, Staff+)

About Kustomer

Kustomer is the industry leading conversational CRM platform perfecting every customer experience.  Built with intelligent tools such as AI and Automation, no code-configuration and a connected data platform that unifies data from multiple sources through a single timeline, Kustomer empowers businesses to operate with greater efficiency and deliver more personalized service to customers across any channel, making every interaction more meaningful and memorable. Today, Kustomer is the core platform for leading brands like Abercrombie, Nuts.com, Skims, Turo, Priceline and Sweetgreen.

Kustomer was founded in 2015 by serial entrepreneurs Brad Birnbaum and Jeremy Suriel and has raised over $200M in funding backed by leading VCs. Meta announced its intention to acquire Kustomer in 2020 and completed the transaction in 2022. Kustomer joined Meta’s Business Messaging Group to transform the way people and businesses communicate through modern messaging channels.  In 2023, Kustomer spun out from Meta as a standalone company backed by original partners, Battery, Redpoint and Boldstart Ventures, who have invested $60M in capital, ensuring Kustomer’s growth and success for many years to come. 

Our Krew is made up of passionate and collaborative people who really care about what they do and the people they help. We look for people who are dedicated to enhancing the customer service experience for everyone involved, as it's the core of what we do. We're growing our business with no plans of slowing down. We actively seek individuals who want to learn and be challenged every day. Kustomer has a strong NYC presence and is also a remote friendly company, with Krew members located throughout the US and United Kingdom. We believe in togetherness to help foster strong relationships, collaboration and communication, and our Krew  gets together for both KKO and Kamp Kustomer  each year.

About the Role

Kustomer is seeking an AI Platform Engineer to architect and build the next generation of our agentic customer service platform. This role sits at the intersection of AI systems engineering, distributed computing, and semantic data processing. You'll drive the development of autonomous AI agents, intelligent tool integrations, and the observability infrastructure that powers our AI-first platform. This position requires deep understanding of agentic architectures, modern AI frameworks, and the ability to envision and implement the future of human-AI collaborative systems.

What We're Seeking

We seek engineers who combine technical excellence with genuine passion for their craft. Our ideal candidate is someone who approaches challenges with curiosity and intrinsic motivation; working on problems not just because they're assigned, but because they find the work genuinely engaging and meaningful.

You should be someone who is naturally detail-oriented, professional, and brings a structured approach to your work while remaining open-minded and adaptable to new technologies and methodologies. We value candidates who work hard and maintain high personal standards, driven by ambition to grow both technically and professionally.

Most importantly, we're looking for engineers who are passionate about the technology space we operate in, particularly AI and automation. You should be excited about the problems we're solving and eager to contribute to innovative solutions that push the boundaries of what's possible.

If you're someone who takes ownership of your work, approaches challenges with intellectual curiosity, and thrives in environments where your passion for technology can directly impact product development, we'd love to hear from you.

What You'll Do:

• Design and implement agentic systems spanning our three core focus areas: AI Engine architecture, Tools & Integrations, and Configuration & Observability

• Build autonomous AI agents with sophisticated reasoning capabilities, agent-to-agent communication protocols, and hierarchical orchestration patterns

• Develop semantic data pipelines for information retrieval, embedding generation, knowledge graph construction, and real-time context synthesis

• Architect tool integration ecosystems using Model Context Protocol (MCP) and function calling frameworks to enable agent-system interactions

• Implement distributed AI workflows managing agent lifecycles, long-term memory systems, and multi-agent collaboration patterns

• Build observability and evaluation frameworks for monitoring agent behavior, measuring AI system performance, and ensuring reliability

• Design machine-first APIs optimized for agen