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. We work in tight, skilled teams, prioritize clarity over complexity, and continuously evolve through curiosity, data, and craftsmanship. We’re seeking technologists and problem solvers who thrive in fast-paced environments, love collaborating with great talent, and approach every day like it’s Day 1. We're a globally diverse team with hubs in New York City, Mountain View, Latin America, and India—embracing both hybrid and remote work to bring the best minds together, wherever they are. If you're driven by continuous learning, rapid pivots, and the challenges of building in a high-growth startup, we’d love to talk. This is more than a job—it’s a journey. 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 Benefits include: Competitive compensation with stock options Comprehensive medical, vision, and dental insurance 401k matching Fitness and wellness stipend Mental well-being benefits Professional learning and development stipend Parental leave, including adoptive and foster parents 3 weeks paid time off (increases with tenure) along with sick leave, bereavement and jury duty ASAPP is committed to creating a diverse environment and is proud to be an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, gender, gender identity or expression, sexual orientation, national origin, disability, age, or veteran status. If you have a disability and need assistance with our employment application process, please email us at [email protected] to obtain assistance. #LI-SL1 #LI-Hybrid 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

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