Maple
ML Research Engineer
New York, NY (HQ) · Exec
Sponsorship not specifiedDetected 465 days ago
CI/CDMachine LearningPyTorchNLPLLMsRAGAgentic AIA/B TestingElectrical EngineeringResearchCollaboration
About the role
- As an ML Research Engineer at Maple, you'll be a part of our core product team transforming cutting-edge research into production-ready voice agents, serving millions of interactions for local businesses.
- We work in person, 5 days a week in our NYC office.
- Collaboration here is fast, noisy (in the best way), and high-trust.
Responsibilities
- Optimize speech recognition (ASR), large language models (LLMs), and text-to-speech (TTS) for real-world use, ensuring accuracy in diverse, noisy environments.
- Create human-in-the-loop and automated systems to monitor performance, detect anomalies, and continuously improve models from real-world feedback.
- Develop pipelines to construct knowledge graphs from business data, powering adaptive AI interactions.
- Manage rapid experimentation, training, and highly optimized production inference.
- Lead evaluations, error analysis, and iterative improvements to maintain robustness and scalability.
- Collaborate with experts from Google Brain, Two Sigma, Stanford, MIT, Columbia, and IBM, rapidly deploying advanced models and systems that directly impact small businesses.
Requirements
- 3-7+ years deploying impactful ML models, ideally in voice, NLP, knowledge graphs, or agent systems.
- Proven ability to minimize latency and resource use on GPUs/TPUs or edge hardware.
- BS, MS, or PhD in Computer Science, Electrical Engineering, Mathematics, or equivalent practical expertise.
Nice to have
- Proficiency in PyTorch or JAX
- optimization experience with CUDA/Triton preferred.
Compensation
- Competitive salary + meaningful equity
Benefits
- Competitive salary + meaningful equity
- Full health, dental, vision, 401k, life insurance, and unlimited PTO
- Fine-tune LLMs with retrieval-augmented generation (RAG), reinforcement learning (RL), and prompt engineering for dynamic, context-aware conversations.
Company info
- we're building automated ontologies that model how businesses actually operate - their services, workflows, constraints, and language - so our agents can adapt to them instantly.
- We meet businesses where they are, not where software wants them to be.
- We have many customers, strong revenue growth, years of runway, and backing from world-class investors.
- I'll share more once we meet.
This listing is sourced directly from Maple's careers page and normalized into a canonical job model.