Lead AI/ML Engineer

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 What you'll do

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

What you'll need

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

What we'd like to see

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