Machine Learning Engineer, Siri Speech

This role is based in Cambridge, England.

Responsibilities:

• Design, train, and evaluate machine learning models for production use cases.
• Build and maintain scalable ML pipelines (data ingestion, feature engineering, training, evaluation, serving).
• Collaborate with data scientists to translate research prototypes into robust, production-grade systems.
• Monitor deployed models for performance degradation and data drift.
• Optimize models for latency, throughput, and resource efficiency.
• Contribute to ML infrastructure, tooling, and best practices.

Minimum Qualifications:

• MSc in Computer Science, Machine Learning, Statistics, or a related field.
• Proven experience in machine learning or a related engineering role.
• Strong proficiency in Python and ML frameworks (PyTorch, TensorFlow, JAX).
• Experience with the full ML lifecycle: data processing, training, evaluation, deployment.

Preferred Qualifications:

• Familiarity with distributed training and large-scale data pipelines.
• Solid understanding of ML fundamentals: supervised/unsupervised learning, model evaluation, regularization.
• Experience with cloud platforms (AWS, GCP, or Azure) and containerization (Docker, Kubernetes).
• Strong software engineering practices: testing, code review, version control.
• Experience with LLMs, fine-tuning, RLHF.
• Familiarity with MLOps tools (MLflow, Weights & Biases, Kubeflow).
• Background in a specific domain (audio generation, speech-to-speech, NLP).
• Experience with feature stores or real-time serving infrastructure.