Lead AI/ML Engineer
Team: Engineering
Location: New York, Mountain View
Commitment: Full-time
Workplace Type: hybrid
Salary:
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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