Machine Learning Engineer

Join our core AI team as a Senior/Staff Machine Learning Engineer, where you'll lead projects to build and deploy cutting-edge AI technology. You'll work alongside industry-veteran scientists and engineers, architecting and evolving large-scale systems that power mission-critical product experiences. You'll also own end-to-end ML system lifecycles, partner with product and infrastructure teams, and drive system-level improvements. Additionally, you'll mentor and elevate other engineers, influencing team standards and contributing to a culture of strong technical decision-making and execution.

• Architect, build, and evolve large-scale SID / ASR / NLP / LLM systems that power mission-critical product experiences.
• Lead the design and implementation of training, fine-tuning, post-training, and inference strategies for large language and speech models.
• Own end-to-end ML system lifecycles, from research prototyping through production deployment, monitoring, iteration, and long-term maintenance.
• Has significant experience with at least one of the following areas: (1) Speech recognition (ASR), (2) Text-to-speech (TTS), (3) Multimodal (speech/text) foundation models, or (4) modern LLM NLP tasks (e.g., summarization, dialogue, speech understanding), especially in real-world production settings
• Is comfortable working with large-scale speech and conversational datasets, including data preprocessing, augmentation, quality analysis, and labeling strategies to support model training and evaluation
• Has experience with or strong interest in agentic systems, tool-use frameworks, or multi-model orchestration
• Has deep, hands-on experience building, fine-tuning, and post-training large language models or other foundation models, including an understanding of failure modes and trade-offs
• Can lead technical projects independently, driving clarity in ambiguous problem spaces and making sound architectural decisions
• Holds a Bachelor’s or Master’s degree in Computer Science or a related field with 3+ years of relevant industry experience; PhD is preferred
• Demonstrates strong command of modern ML research, with the ability to critically evaluate new papers and decide what is production-worthy versus experimental
• Has extensive experience deploying, monitoring, and operating ML systems in production, including model versioning, rollback strategies, and performance regression detection
• Has interest in creating innovation and advancing applied research
• Is highly effective at cross-functional collaboration, working end-to-end with product, infra, research, and data teams to deliver outcomes—not just models
• Experience with personalization, recommendation systems, or user modeling is a plus
• Has experience scaling ML systems across training, inference, and serving infrastructure while balancing cost, latency, and reliability constraints