About the job
Hello! I’m Aidan, the founder of Maple.
At Maple, we are on a mission to revolutionize local businesses by creating intelligent AI agents. Our agents efficiently manage tasks such as answering calls, taking orders, and booking appointments, providing seamless interactions through natural voice communication.
Our deeper mission: We are developing automated ontologies that accurately reflect the operational realities of businesses — capturing their services, workflows, constraints, and unique language — allowing our agents to adapt instantly to diverse business needs. We strive to meet businesses where they are, rather than imposing a rigid software framework.
With a growing customer base, strong revenue growth, a secure financial runway, and support from esteemed investors, we are positioned for exciting advancements. More details will be shared during our conversation.
Role Overview
As a Machine Learning Research Engineer at Maple, you will be integral to our core product team, transforming innovative research into production-ready voice agents that facilitate millions of customer interactions for small businesses. You will collaborate with experts from prestigious institutions such as Google Brain, Two Sigma, Stanford, MIT, Columbia, and IBM to swiftly deploy sophisticated models and systems that have a direct impact on small enterprises.
Our team operates in person, five days a week in our vibrant NYC office. Our collaborative environment is characterized by rapid-paced, dynamic interactions filled with trust and transparency. We embrace a culture of speed and adaptability, tackling challenges head-on.
Key Responsibilities
Enhance automatic speech recognition (ASR), large language models (LLMs), and text-to-speech (TTS) systems for practical applications, ensuring high accuracy in diverse and noisy environments.
Fine-tune LLMs utilizing retrieval-augmented generation (RAG), reinforcement learning (RL), and prompt engineering for engaging, contextually aware dialogues.
Integrate AI components into autonomous agents capable of executing complex tasks such as scheduling, order management, and problem resolution.
Design human-in-the-loop and automated systems for monitoring performance, identifying anomalies, and iteratively improving models based on real-world feedback.
Develop pipelines for building knowledge graphs from business data, enhancing dynamic AI interactions.
Collaborate with infrastructure teams to scale models effectively, ensuring robust performance and reliability.

