MLops Engineer

Play a part in shaping the future of human-computer interaction. As an MLOps Engineer, you will be the backbone of the machine learning infrastructure that powers our speech, audio, and conversational AI teams - ensuring their models are trained on the best possible data. You will bridge the gap between research, data science, and engineering, owning the full ML lifecycle from large-scale data pipelines and distributed GPU training through to low-latency, high-fidelity inference and optimization. You'll partner closely with Audio ML Engineers, Speech ML Engineers, and ML Data Scientists to remove friction across their workflows and accelerate the path from research to product.

Description

The MLOps Engineer will drive end-to-end quality and operational excellence across data ingestion, model training, deployment pipelines, and MLOps tooling for our speech and audio ML platforms. This hire will build, deploy, and optimize production-grade systems with a strong emphasis on scalable, GPU-accelerated infrastructure. You will own the training infrastructure that powers distributed and self-supervised model training on HPC and Slurm-managed clusters, as well as the inference pipelines that bring low-latency, high-fidelity audio and speech models to production. You will establish standard methodologies for model integration, deployment, monitoring, and reproducibility using CI/CD principles.

Minimum Qualifications

3 years in software engineering with demonstrated experience in large-scale software system design and implementation
Bachelor's Degree in Software Engineering, Computer Science, Electrical Engineering, Statistics, Machine Learning, Operations Research, or a related field
Proven track record of shipping and maintaining production-grade ML systems end-to-end
Hands-on experience with GPU-based model training and inference, including distributed/multi-node training
Experience operating workloads on HPC environments and job schedulers such as Slurm
Proficiency in Python and familiarity with deep learning frameworks such as PyTorch, TensorFlow, or JAX

Preferred Qualifications

Experience supporting speech and audio ML pipelines (e.g., ASR, TTS, speaker recognition, voice isolation, generative speech) and large-scale audio data processing
Experience with infrastructure for self-supervised and large-model training
Deep familiarity with GPU performance tuning, mixed-precision training, and distributed training frameworks
Familiarity with data quality frameworks, model monitoring, drift detection, and observability practices in production
Experience optimizing models for on-device or Apple silicon inference