At a Glance
- Tasks: Build cutting-edge software for machine learning on Apple Silicon.
- Company: Join the innovative team at Apple Cloud Platform.
- Benefits: Competitive salary, health benefits, and opportunities for professional growth.
- Other info: Dynamic environment with a focus on collaboration and innovation.
- Why this job: Work on groundbreaking AI projects that shape the future of technology.
- Qualifications: Experience in machine learning, coding in Swift/C++, and system optimisation.
The predicted salary is between 70000 - 90000 £ per year.
As part of Apple Cloud Platform, our team is responsible for building libraries and services which form the foundations of critically important systems at Apple. Specifically, as a ML Systems Engineer, you will focus on building software stack to run large machine learning models (generative AI, LLMs) over multiple high performance Apple Silicon SoCs.
Key Qualifications
- Practical experience running and evaluating machine learning models for quality and performance metrics
- Experience in system-level code optimisation and power/performance evaluation for ML acceleration hardware
- Large-scale server side development experience
- Experience programming in Swift, C, C++, iOS/macOS, XCode
- Bonus qualification: familiar with Apple ML stack (ANE, CoreML, MPS/Metal), high-level general distributed ML stack (PyTorch-distributed, NCCL) and high throughput inter-chip communication systems.
Responsibilities and Work
We write software in Swift and C++ to build services and infrastructure around innovative generative AI and machine learning. We write performant and scalable frameworks to distribute and coordinate ML inference tasks to different hardware acceleration IP blocks on different SoCs; we build jobs to deploy and load models, and support high level machine learning platforms. Our software has a growing user base and the team is looking to expand to build amazing products making it a truly exciting place to work.
Education & Experience
- B.S. in Computer Science or other numerate subject, with 6+ years experience; or M.S. in Computer Science or other numerate subject, with 4+ years experience
Additional Requirements
- Quality focus - produce reliable, maintainable, deliverable software
- Comfortable diving deep - working across multiple levels of abstraction
- Good at handling relationships & communication - collaborate well with colleagues across a wide range of functions.
Machine Learning Systems Engineer employer: Apple Inc.
Apple is an exceptional employer, offering a dynamic work culture that fosters innovation and collaboration among talented individuals. As a Machine Learning Systems Engineer, you will have access to cutting-edge technology and the opportunity to contribute to groundbreaking projects in a supportive environment that prioritises employee growth and development. Located in a vibrant tech hub, Apple provides unique advantages such as competitive benefits, a commitment to work-life balance, and the chance to be part of a team that is shaping the future of technology.
StudySmarter Expert Advice🤫
We think this is how you could land Machine Learning Systems Engineer
✨Tip Number 1
Network like a pro! Reach out to folks in the industry, especially those at Apple or similar companies. Attend meetups, webinars, or even online forums where you can chat about machine learning and software engineering. You never know who might have a lead on your dream job!
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your projects, especially those involving Swift, C++, or machine learning models. Having tangible examples of your work can really set you apart when you're chatting with potential employers.
✨Tip Number 3
Prepare for technical interviews by brushing up on system-level code optimisation and performance evaluation. Practice coding challenges that focus on ML acceleration hardware. The more prepared you are, the more confident you'll feel during those tricky interview questions!
✨Tip Number 4
Don't forget to apply through our website! We love seeing applications directly from candidates who are passionate about what we do. Plus, it gives you a chance to highlight your enthusiasm for building innovative AI solutions at Apple.
We think you need these skills to ace Machine Learning Systems Engineer
Some tips for your application 🫡
Tailor Your CV:Make sure your CV highlights your experience with machine learning models and system-level code optimisation. We want to see how your skills align with the role, so don’t be shy about showcasing relevant projects!
Craft a Compelling Cover Letter:Your cover letter is your chance to tell us why you’re the perfect fit for the Machine Learning Systems Engineer position. Share your passion for generative AI and any specific experiences that relate to our work at Apple Cloud Platform.
Showcase Your Technical Skills:Be sure to mention your programming experience in Swift, C, and C++. If you’ve worked with Apple’s ML stack or distributed ML frameworks, let us know! We love seeing candidates who are well-versed in the tools we use.
Apply Through Our Website:We encourage you to apply directly through our website. It’s the best way for us to receive your application and ensures you’re considered for the role. Plus, it’s super easy to do!
How to prepare for a job interview at Apple Inc.
✨Know Your Tech Inside Out
Make sure you’re well-versed in the technologies mentioned in the job description, especially Swift and C++. Brush up on your knowledge of machine learning models and Apple’s ML stack. Being able to discuss specific projects or experiences where you've applied these skills will really impress the interviewers.
✨Showcase Your Problem-Solving Skills
Prepare to discuss how you've tackled challenges in system-level code optimisation and performance evaluation. Think of examples where you improved the efficiency of a machine learning model or optimised code for hardware acceleration. This will demonstrate your practical experience and analytical thinking.
✨Communicate Clearly and Collaboratively
Since the role involves working with various teams, practice articulating your thoughts clearly. Be ready to explain complex concepts in a way that’s easy to understand. Highlight any past experiences where you successfully collaborated with cross-functional teams to achieve a common goal.
✨Ask Insightful Questions
Prepare some thoughtful questions about the team’s current projects or future goals. This shows your genuine interest in the role and helps you gauge if it’s the right fit for you. Questions about their approach to generative AI or how they handle model deployment can spark engaging discussions.
