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

Apple
Cambridge
1 year ago
Applications closed

Related Jobs

View all jobs

AI/MLOps Platform Engineer

Machine Learning Engineer

AI/ML Data Scientist - Drive Cross-Dept Insights

Lead MLOps Engineer — Scalable AI for Banking

Senior MLOps Engineer: AI-Driven Banking Platform

Senior MLOps 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 to Write an AI Job Ad That Attracts the Right People

Artificial intelligence is now embedded across almost every sector of the UK economy. From fintech and healthcare to retail, defence and climate tech, organisations are competing for AI talent at an unprecedented pace. Yet despite the volume of AI job adverts online, many employers struggle to attract the right candidates. Roles are flooded with unsuitable applications, while highly capable AI professionals scroll past adverts that feel vague, inflated or disconnected from reality. In most cases, the issue isn’t a shortage of AI talent — it’s the quality of the job advert. Writing an effective AI job ad requires more care than traditional tech hiring. AI professionals are analytical, sceptical of hype and highly selective about where they apply. A poorly written advert doesn’t just fail to convert — it actively damages your credibility. This guide explains how to write an AI job ad that attracts the right people, filters out mismatches and positions your organisation as a serious employer in the AI space.

Maths for AI Jobs: The Only Topics You Actually Need (& How to Learn Them)

If you are a software engineer, data scientist or analyst looking to move into AI or you are a UK undergraduate or postgraduate in computer science, maths, engineering or a related subject applying for AI roles, the maths can feel like the biggest barrier. Job descriptions say “strong maths” or “solid fundamentals” but rarely spell out what that means day to day. The good news is you do not need a full maths degree worth of theory to start applying. For most UK roles like Machine Learning Engineer, AI Engineer, Data Scientist, Applied Scientist, NLP Engineer or Computer Vision Engineer, the maths you actually use again & again is concentrated in a handful of topics: Linear algebra essentials Probability & statistics for uncertainty & evaluation Calculus essentials for gradients & backprop Optimisation basics for training & tuning A small amount of discrete maths for practical reasoning This guide turns vague requirements into a clear checklist, a 6-week learning plan & portfolio projects that prove you can translate maths into working code.

Neurodiversity in AI Careers: Turning Different Thinking into a Superpower

The AI industry moves quickly, breaks rules & rewards people who see the world differently. That makes it a natural home for many neurodivergent people – including those with ADHD, autism & dyslexia. If you’re neurodivergent & considering a career in artificial intelligence, you might have been told your brain is “too much”, “too scattered” or “too different” for a technical field. In reality, many of the strengths that come with ADHD, autism & dyslexia map beautifully onto AI work – from spotting patterns in data to creative problem-solving & deep focus. This guide is written for AI job seekers in the UK. We’ll explore: What neurodiversity means in an AI context How ADHD, autism & dyslexia strengths match specific AI roles Practical workplace adjustments you can ask for under UK law How to talk about your neurodivergence during applications & interviews By the end, you’ll have a clearer picture of where you might thrive in AI – & how to set yourself up for success.