A
Asapp 21mo ago
USD 170000–190000/yr

Lead AI/ML Engineer

United StatesUnited StatesFull-timelead
Data ScienceOtherMachine Learning EngineerData
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Quick Summary

Overview

At ASAPP, our mission is simple: deliver the best AI-powered customer experience—faster than anyone else. To achieve that, we’re guided by principles that shape how we think, build, and execute.

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Data ScienceOtherMachine Learning EngineerData
At ASAPP, our mission is simple: deliver the best AI-powered customer experience—faster than anyone else. To achieve that, we’re guided by principles that shape how we think, build, and execute. We value customer obsession, purposeful speed, ownership, and a relentless focus on outcomes. ASAPP’s AI Engineering team is seeking an enterprising, talented and curious machine learning engineer. 

We are seeking a highly experienced Lead AI/ML Engineer to join our Core GenerativeAgent team. You will play a pivotal role in designing, building, and deploying cutting-edge AI systems that power mission-critical enterprise applications. This role is ideal for an individual who thrives in ambiguity, is deeply technical, and has a strong product sense paired with deep expertise in foundational models and enterprise AI systems.

You will lead the design and delivery of end-to-end voice AI solutions, combining large language models with speech technologies such as speech-to-text, text-to-speech, and real-time streaming audio pipelines. This role requires a hands-on technical leader who can architect low-latency, highly reliable conversational voice systems and guide a team through ambiguity toward production excellence.

We are looking for someone who understands the unique constraints of voice experiences, latency, turn-taking, interruption handling, streaming inference, and audio quality, and can translate these into scalable, enterprise-grade systems.

This is a hybrid role with weekly in-person responsibilities. We have offices in New York City and Mountain View, CA
  • Build real-time conversational AI systems, including voice interfaces powered by speech-to-text, text-to-speech, and streaming inference pipelines
  • Design and optimize low-latency inference workflows for multimodal applications involving text, speech, and real-time interactions
  • Integrate and apply foundation models from major providers (OpenAI, AWS Bedrock, Anthropic, etc.) for prototyping and production use cases
  • Adapt, evaluate, and optimize LLMs for domain-specific enterprise applications
  • Build and maintain infrastructure for experimentation, deployment, and monitoring of AI models in production
  • Improve model performance and inference workflows with attention to latency, cost, and reliability
  • Provide technical leadership within the team, mentoring engineers and promoting best practices in ML engineering
  • Partner with product and cross-functional stakeholders to translate requirements into scalable ML solutions
  • Contribute to the evolution of internal standards for experimentation, evaluation, and deployment
  • 6+ years of experience in Machine Learning or AI systems, with hands-on experience in LLMs, speech, or conversational AI systems
  • Experience building on integrating speech-to-text and text-to-speech systems
  • Strong experience integrating voice models into production applications
  • Proficiency on Python and ML frameworks like PyTorch or TensorFlow
  • Proven experience leading complex, cross-functional AI initiatives
  • Deep understanding of latency-sensitive system design and distributed architectures
  • Strong proficiency in Python and ML frameworks such as PyTorch or TensorFlow
  • Understanding of RAG pipelines, prompt engineering, and vector search
  • Experience deploying and scaling AI systems using AWS (required), Docker, Kubernetes, and CI/CD practices
  • Strong communication skills with the ability to align engineering, product, and executive stakeholders
  • Comfortable operating in fast-paced environments and driving clarity in ambiguous problem spaces
  • Experience with speech model fine-tuning and acoustic/language model optimization
  • Experience with production applications of S2S models
  • Hands-on experience with real-time or streaming audio systems (WebRTC, gRPC streaming, or similar architectures)
  • Experience optimizing TTS prosody, pronunciation control, and voice customization
  • Background in MLOps, experimentation platforms, or evaluation frameworks for speech and conversational systems
  • Contributions to open-source AI or speech tooling
  • Graduate degree (MS or PhD) in Computer Science, Machine Learning, Speech Processing, or related field

    • Listing Details

    Posted
    March 5, 2026
    First seen
    March 26, 2026
    Last seen
    April 24, 2026

    Posting Health

    Days active
    28
    Repost count
    0
    Trust Level
    37%
    Scored at
    April 24, 2026

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