AI/ML - Data Engineer (NLP/Speech), Siri and Information Intelligence

Apple
Cambridge
1 year ago
Applications closed

Related Jobs

View all jobs

MLOps Engineer

MLOps Engineer

Lead Software Engineer - MLOps Platform

Senior Lead Analyst - Data Science_ AI/ML & Gen AI

Data Science Practitioner

Machine Learning Engineer

Summary:
Play a part in the next revolution in human-computer interaction. Contribute to a product that is redefining mobile computing. Create groundbreaking technology for large scale systems, natural language, big data, and artificial intelligence. And work with the people who created the intelligent assistant that helps millions of people get things done — just by asking. Join the Siri Response / Text-to-Speech (TTS) team at Apple. Our team is looking for exceptional data engineers passionate about delivering delightful customer experiences with Siri voices. As Data Engineer (NLP/Speech), you'll work on building and maintaining text and speech datasets, processes and workflows for our TTS systems.
Key Qualifications:
5+ years’ industry experience processing large-scale text/speech datasets for ML applicationsStrong expertise in Python, (NoSQL) databases, cloud-based data technologies, and working with large datasets and pipelinesExperience in tooling and streamlining workflows in complex processesHighly-motivated, creative, organized and a strong problem solverOutstanding spoken and written communication skills
Description:
Apple is hiring data engineers for the Siri Response / Text-to-Speech (TTS) team. You'll be working at the frontier of AI, processing massive amounts of speech and text data for our TTS systems. You'll work closely with fellow engineers to gather and integrate new speech and text data into our repositories, transforming raw data into formats usable for TTS model training, and making datasets available to partner teams in Apple to power Siri's voice. Your responsibilities will include: * Collect and centralize data from various sources, working with internal privacy, legal and modeling teams* Build processes and workflows that support data transformation for TTS systems (e.g. audio processing and text annotation), based on the needs and requirements of modeling teams* Provide datasets to partner teams, managing access or usage control* Create dashboard for interactive data exploration* Develop tools and tests to ensure quality and help diagnose issues* Perform analysis on external and internal processes and data to identify opportunities for improvement* Develop prototype ML models utilizing in-house toolkits If this sounds like you, we'd love to hear from you!
Additional Requirements:
* Experience in working with natural language data, lexical resources, corpora, NLP algorithms and tools is a plus* Experience in machine learning, natural language processing, machine translation or text-to-speech is a plus* Knowledge of one or more foreign languages is a plus

Subscribe to Future Tech Insights for the latest jobs & insights, direct to your inbox.

By subscribing, you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

How Many AI Tools Do You Need to Know to Get an AI Job?

If you are job hunting in AI right now it can feel like you are drowning in tools. Every week there is a new framework, a new “must-learn” platform or a new productivity app that everyone on LinkedIn seems to be using. The result is predictable: job seekers panic-learn a long list of tools without actually getting better at delivering outcomes. Here is the truth most hiring managers will quietly agree with. They do not hire you because you know 27 tools. They hire you because you can solve a problem, communicate trade-offs, ship something reliable and improve it with feedback. Tools matter, but only in service of outcomes. So how many AI tools do you actually need to know? For most AI job seekers: fewer than you think. You need a tight core toolkit plus a role-specific layer. Everything else is optional. This guide breaks it down clearly, gives you a simple framework to choose what to learn and shows you how to present your toolset on your CV, portfolio and interviews.

What Hiring Managers Look for First in AI Job Applications (UK Guide)

Hiring managers do not start by reading your CV line-by-line. They scan for signals. In AI roles especially, they are looking for proof that you can ship, learn fast, communicate clearly & work safely with data and systems. The best applications make those signals obvious in the first 10–20 seconds. This guide breaks down what hiring managers typically look for first in AI applications in the UK market, how to present it on your CV, LinkedIn & portfolio, and the most common reasons strong candidates get overlooked. Use it as a checklist to tighten your application before you click apply.

The Skills Gap in AI Jobs: What Universities Aren’t Teaching

Artificial intelligence is no longer a future concept. It is already reshaping how businesses operate, how decisions are made, and how entire industries compete. From finance and healthcare to retail, manufacturing, defence, and climate science, AI is embedded in critical systems across the UK economy. Yet despite unprecedented demand for AI talent, employers continue to report severe recruitment challenges. Vacancies remain open for months. Salaries rise year on year. Candidates with impressive academic credentials often fail technical interviews. At the heart of this disconnect lies a growing and uncomfortable truth: Universities are not fully preparing graduates for real-world AI jobs. This article explores the AI skills gap in depth—what is missing from many university programmes, why the gap persists, what employers actually want, and how jobseekers can bridge the divide to build a successful career in artificial intelligence.