At a Glance
- Tasks: Design and build machine learning pipelines to enhance developer workflows.
- Company: Join Apple’s innovative Insight & Release Technologies team in London.
- Benefits: Competitive salary, inclusive culture, and opportunities for personal growth.
- Other info: Collaborative environment with a focus on diversity and inclusion.
- Why this job: Be at the forefront of AI-driven solutions that impact millions of users.
- Qualifications: Experience in ML engineering and software development is essential.
The predicted salary is between 70000 - 90000 € per year.
Do you want to help define the future of delivering Apple software to customers? Join the Insight & Release Technologies team to work on new technologies that will be used to deliver Apple platforms to millions of customers. Our team has a passion for innovation and engineering and is looking for individuals with a genuine enthusiasm for collaborating with others to solve sophisticated problems with a focus on the user experience.
You will join a team working on the next generation of software release workflows that enable the software development lifecycle for an ever-growing number of platforms and teams contributing to software products. Our applications integrate with developers’ workflows to enable the software development lifecycle from integrating source code all the way to releasing Apple platforms and assets to customers. In this role, you will work on bringing AI to our developer productivity tools. You will be at the forefront of building intelligent, agentic systems that help Apple engineers write better, higher-quality code. You will collaborate closely with data, platform, and infrastructure teams to identify high-impact opportunities where ML can meaningfully improve developer workflows.
Responsibilities
- Design and build machine learning pipelines.
- Evaluate, integrate, and optimize ML models and agentic workflows.
- Develop intelligent systems that reason over code coverage data to surface meaningful insights, prioritize under-tested areas, and recommend targeted test strategies.
- Build and improve semantic code search capabilities that allow engineers to find relevant code, patterns, and examples across large-scale internal codebases using natural language and embedding-based retrieval.
- Apply ML techniques to advance static analysis tooling, including smarter bug detection, vulnerability identification, and code smell classification, beyond what traditional rule-based approaches can achieve.
- Partner with platform and product teams to deeply understand engineer pain points and translate them into practical, high-impact ML solutions.
- Lead the design of generative AI solutions, optimize ML infrastructure, and guide the development of data preparation and model optimization strategies.
- Drive technical direction, facilitate alignment across organizations, and mentor engineers across the team.
Minimum Qualifications
- Experience in ML engineering and software development, including experience in system design, architecture, and shipping scalable software products.
- Strong software engineering fundamentals (APIs, system design, distributed systems, frameworks architecture).
- Experience leading technical project strategy and optimizing ML infrastructure.
Preferred Qualifications
- Experience with GenAI techniques or GenAI-related concepts.
- Bachelor's degree in Computer Science, Machine Learning, or equivalent practical experience.
At Apple, we’re not all the same. And that’s our greatest strength. We draw on the differences in who we are, what we’ve experienced and how we think. Because to create products that serve everyone, we believe in including everyone. Therefore, we are committed to treating all applicants fairly and equally. As a registered Disability Confident employer, we will work with applicants to make any reasonable accommodations. Apple will consider for employment all qualified applicants with criminal backgrounds in a manner consistent with applicable law.
At Apple, we believe accessibility is a fundamental human right. You’ll find that idea reflected in everything here — in our culture, our benefits and our digital tools. By welcoming as many perspectives as possible, we help you build a career where you feel like you belong.
Senior Machine Learning Engineer, Agentic Workflows - Software Delivery in London employer: Apple Inc.
Apple is an exceptional employer that fosters a culture of innovation and collaboration, making it an ideal place for a Senior Machine Learning Engineer to thrive. Located in London, you will have access to cutting-edge technologies and the opportunity to work alongside passionate professionals dedicated to enhancing developer productivity through AI. With a strong commitment to diversity and inclusion, Apple offers meaningful career growth opportunities and a supportive environment where every voice is valued.
StudySmarter Expert Advice🤫
We think this is how you could land Senior Machine Learning Engineer, Agentic Workflows - Software Delivery in London
✨Tip Number 1
Network like a pro! Reach out to folks in the industry, attend meetups, and connect with people on LinkedIn. You never know who might have the inside scoop on job openings or can refer you directly.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your machine learning projects and contributions. This is your chance to demonstrate your expertise and passion for the field, making you stand out to potential employers.
✨Tip Number 3
Prepare for interviews by brushing up on your technical knowledge and problem-solving skills. Practice coding challenges and be ready to discuss your past projects in detail. Confidence is key!
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets seen. Plus, we love seeing candidates who are proactive about their job search.
We think you need these skills to ace Senior Machine Learning Engineer, Agentic Workflows - Software Delivery in London
Some tips for your application 🫡
Show Your Passion for Innovation:When writing your application, let your enthusiasm for innovation and engineering shine through. We love seeing candidates who are genuinely excited about collaborating to solve complex problems, especially in the realm of machine learning.
Tailor Your Experience:Make sure to highlight your relevant experience in ML engineering and software development. We want to see how your background aligns with our focus on building intelligent systems and optimising workflows, so be specific about your past projects and achievements.
Be Clear and Concise:Keep your application clear and to the point. We appreciate well-structured applications that communicate your skills and experiences effectively. Avoid jargon unless it’s necessary, and make sure your passion for the role comes across without fluff.
Apply Through Our Website:Don’t forget to apply through our website! It’s the best way for us to receive your application and ensures you’re considered for the role. Plus, it shows you’re serious about joining our team at StudySmarter.
How to prepare for a job interview at Apple Inc.
✨Know Your ML Fundamentals
Brush up on your machine learning fundamentals, especially those relevant to software delivery and agentic workflows. Be prepared to discuss your experience with ML pipelines, model optimisation, and how you've applied these in real-world scenarios.
✨Showcase Your Collaboration Skills
Since this role involves working closely with various teams, highlight your collaboration experiences. Share specific examples of how you’ve partnered with data, platform, or infrastructure teams to solve complex problems and improve workflows.
✨Prepare for Technical Questions
Expect technical questions that assess your understanding of system design and architecture. Be ready to explain your approach to building scalable software products and optimising ML infrastructure, as well as any challenges you faced and how you overcame them.
✨Demonstrate Your Passion for Innovation
Express your enthusiasm for innovation and how it drives your work. Discuss any projects where you’ve implemented generative AI techniques or advanced static analysis tooling, and how these innovations improved developer productivity or code quality.
