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MLops Engineer

Apple· Machine Learning and AI
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HerzliyaFull-timePosted 4mo ago

About the role

MLOps Engineer responsible for the machine learning infrastructure powering speech, audio, and conversational AI teams. Owns end-to-end ML lifecycle from data pipelines and GPU-based training to low-latency inference, optimization, and CI/CD-driven deployment. Collaborates with Audio ML Engineers, Speech ML Engineers, and ML Data Scientists to streamline workflows and accelerate research-to-product delivery.

Responsibilities

  • Design, build, and operate large-scale data pipelines for proprietary audio and speech datasets - supporting curation, quality monitoring, and validation at scale alongside our ML Data Science team.
  • Partner closely with Audio ML Engineers, Speech ML Engineers, ML Data Scientists, and product teams to define metrics, gather requirements, and bring new capabilities to life.
  • Build and operate distributed GPU training workflows, including job scheduling and resource management on Slurm-managed HPC clusters, for both supervised and self-supervised methods.
  • Optimize model inference for low latency and high-fidelity streaming across serving environments, including optimization for Apple silicon.
  • Design and maintain automated pipelines for model training, evaluation, versioning, and deployment, with special attention to speech, audio, and signal-processing workflows.
  • Identify and resolve bottlenecks in ML and data workflows, improving system reliability, latency, and throughput at scale.

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
  • 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

Skills

Required:Data pipeline designGPU-based trainingInference optimizationPythonDeep learning frameworks

Benefits

  • Accessibility-focused culture and benefits (per Apple’s commitment)
Last updated Jun 26, 2026. Interested? Apply on the company site.

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