Machine Learning Engineer

Sendbird is building AI agents for customer experience. Our platform already powers billions of conversations every month across chat, voice, video, and messaging APIs. We are now using that foundation to build agents that understand customer context, reason over business data, and take reliable action in production.

We are looking for a Machine Learning Engineer to research, build, and productionize new capabilities for those agents. This role sits at the intersection of agent product development, applied AI research, and production engineering. You will work on systems that enterprise customers depend on every day, not demos or isolated prototypes.

About Sendbird and delight.ai

Sendbird has spent more than a decade building communication infrastructure for in-app chat, voice, video, and messaging APIs. More than 4,000 brands use our platform, including DoorDash, Match Group, Noom, Yahoo Sports, and Rakuten. Our systems support more than 7 billion messages every month.

In 2024, we made a strategic shift toward AI-first customer experience. In 2025, we launched our enterprise AI agent product, delight.ai. Delight.ai helps businesses deliver customer support and engagement that is faster, more contextual, and more personal. Unlike simple FAQ bots, our agents are built to remember customer context, use tools, retrieve relevant knowledge, connect across channels, and handle real customer workflows with accuracy and control.

The Role

As a Machine Learning Engineer, you will design, build, evaluate, and ship new capabilities for our AI agents. You will work across agent architecture, retrieval, memory, planning, tool use, workflow automation, voice, evaluation, data pipelines, model adaptation, inference, and production integration.

This is a hands-on engineering role for someone who can turn AI research and product ideas into reliable customer-facing features. Some problems will require training, fine-tuning, or adapting models. Others will require better retrieval, better context handling, better tools, stronger evaluation, or a more thoughtful product design. The right person knows how to choose the right lever and ship the result.

What you will work on

· Research, prototype, evaluate, and productionize new agent capabilities, including memory, planning, tool use, workflow execution, reasoning, and context management.

· Improve the quality, reliability, and usefulness of customer-facing AI agents through better retrieval, prompting, evaluation, model adaptation, product behavior, and system design.

· Build the core intelligence layer for our agents, including retrieval systems, memory, tool-calling pipelines, workflow orchestration, and agentic reasoning.

· Train, fine-tune, and adapt LLMs and related models when model-level work is the right way to improve agent quality or product capability.

· Build data pipelines for model training, agent evaluation, and product improvement, including labeling workflows, dataset construction, quality checks, and feedback loops.

· Design evaluation systems that measure task completion, accuracy, latency, cost, reliability, safety, customer experience quality, and production regressions.

· Optimize inference systems for production, including model selection, serving architecture, batching, caching, latency, throughput, and cost.

· Integrate voice AI, internal tools, third-party APIs, and workflow systems so agents can take useful actions across real customer journeys.

· Partner with product managers, engineers, and customer-facing teams to turn ambiguous AI product requirements into shippable agent features.

What we are looking for

· 5+ years of professional experience in ML engineering, machine learning, data science, or backend engineering with substantial AI/ML ownership.

· Experience building AI-powered product features for real users, preferably involving agents, conversational AI, workflow automation, retrieval, or LLM systems.

· Hands-on experience training, fine-tuning, or adapting LLMs or other deep learning models for production use cases.

· Strong practical knowledge of model training workflows, including dataset preparation, experiment tracking, evaluation, model selection, and deployment.

· Experience building production LLM systems, such as RAG, tool calling, agents, model orchestration, prompt systems, or evaluation pipelines.

· Strong Python skills and experience building production-grade services.

· Working knowledge of inference and serving tradeoffs, including latency, throughput, GPU utilization, model size, batching, caching, and cost.

· Experience with ML infrastructure or MLOps tools used for data pipelines, training jobs, model deployment, monitoring, or evaluation.

· Strong debugging instincts for agent systems, including failure analysis, hallucination reduction, retrieval quality, model behavior, tool-use failures, and regressions.

· Clear communication skills, especially when explaining technical tradeoffs to product managers, engineers, and leade