Machine Learning Engineer
Lead projects building and deploying large-scale ASR/NLP/LLM systems for meeting intelligence. Architect training, fine-tuning, and inference pipelines using PyTorch/JAX and own ML systems from research to production.
About the job
Your Impact
- Architect, build, and evolve large-scale SID / ASR / NLP / LLM systems that power mission-critical product experiences including summarization, chat, and speech understanding across millions of conversations.
- Lead the design and implementation of training, fine-tuning, post-training, and inference strategies for large language and speech models using PyTorch and/or JAX, making principled trade-offs across quality, latency, cost, and reliability.
- Design and improve model architectures, loss functions, decoding strategies, and training techniques for speech and language models, informed by both research and production constraints.
- Own end-to-end ML system lifecycles, from research prototyping through production deployment, monitoring, iteration, and long-term maintenance.
- Partner deeply with product, and infrastructure teams to develop and translate cutting-edge research into scalable, production-grade systems that deliver measurable user and business impact.
- Drive system-level improvements in model performance, robustness, observability, and operational excellence using real-world conversational data at scale.
- Set technical direction and best practices for ML infrastructure, data pipelines, evaluation frameworks, and deployment workflows in a cloud environment.
- Identify and resolve complex, ambiguous problems in model behavior, data quality, scaling, and system interactions, often before they surface as user-visible issues.
- Mentor and elevate other engineers, influencing team standards, reviewing designs, and contributing to a culture of strong technical decision-making and execution.
We're Looking for Someone Who
- 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.
- 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.
- Demonstrates strong command of modern ML research, with the ability to critically evaluate new papers and decide what is production-worthy versus experimental.
- Has interest in creating innovation and advancing applied research.
- Has extensive experience deploying, monitoring, and operating ML systems in production, including model versioning, rollback strategies, and performance regression detection.
- 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 scaling ML systems across training, inference, and serving infrastructure while balancing cost, latency, and reliability constraints.
- Is highly effective at cross-functional collaboration, working end-to-end with product, infra, research, and data teams to deliver outcomes—not just models.
- Can lead technical projects independently, driving clarity in ambiguous problem spaces and making sound architectural decisions.
- Has experience with or strong interest in agentic systems, tool-use frameworks, or multi-model orchestration.
- 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.
- Experience with personalization, recommendation systems, or user modeling is a plus.
Skills
PyTorch, JAX, Asr, NLP, LLMs, Machine Learning, Speech Recognition, Model Training, Model Fine-Tuning, Model Deployment
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