Staff Machine Learning Engineer – Apple – Toronto, ON
Location: Toronto, ON | Company: Apple
Apple’s News, Books, and Stocks team in Toronto, Ontario is looking for a Staff Machine Learning Engineer to help shape the next generation of products used by millions of people every day. This is a role for someone who thrives on hard problems, values quality, and wants to work alongside engineers who genuinely care about each other and the work they do.
The team applies machine learning to complex challenges across the news, books, and stocks domains — from text extraction and named entity recognition to search, ranking, and duplicate detection. If you’re excited by the idea of building scalable ML services that directly influence what people read and discover every day, this opportunity is worth a close look.
About the Role: Staff Machine Learning Engineer
As a Staff Machine Learning Engineer, you’ll take a central role in delivering scalable ML-driven services within a distributed, cross-functional team. You’ll work on meaningful features across Apple News, Apple Books, and related apps — crafting models and systems that balance quality content curation, user privacy, and intelligent personalization.
Collaboration is at the heart of how this team operates. You’ll work closely with interdisciplinary partners, contribute to an open and inclusive work environment, and help mentor and inspire teammates along the way. Apple’s commitment to respecting engineers as both professionals and people is reflected in this team’s outstanding retention rate.
Benefits and Salary
Apple is known for offering a comprehensive total compensation package that goes well beyond base pay. While specific salary figures aren’t listed in this posting, Apple typically provides health and dental benefits, an employee stock purchase plan, RRSP support, product discounts, and access to wellness programmes. The role also offers the intellectual reward of working on products that reach millions of users globally.
Job Details
🏢 Company: Apple
📍 Location: Toronto, Ontario
🆔 Requisition ID: 200674861-3350
🗓️ Date Posted: July 29, 2026
Responsibilities
In this role, you’ll be at the forefront of building scalable machine learning services that power some of Apple’s most-used media apps. Your work will span the full ML lifecycle — from research and model design to shipping production-ready features used by millions of people daily.
- Design and deliver scalable ML-powered services across News, Books, and Stocks domains
- Develop and ship machine learning models addressing challenges like text extraction, named entity recognition, and duplicate detection
- Build and refine search and ranking systems to improve content discovery for users
- Collaborate with interdisciplinary partners to design user-facing ML features
- Leverage deep learning toolkits such as PyTorch or TensorFlow to develop robust models
- Contribute to a culture of mutual respect, knowledge sharing, and inclusivity within the team
- Mentor and inspire fellow engineers, supporting the team’s high standards and strong retention culture
Requirements / Skills
Apple is looking for a self-driven, high-energy ML engineer with a proven track record of shipping models in real products. The ideal candidate brings deep technical expertise alongside strong communication skills and a collaborative mindset that uplifts those around them.
- MS in Machine Learning, Computer Science, or a related field — or equivalent industry experience
- At least 5 years of experience shipping machine learning models in production products
- Strong programming skills in Python, Java, or a related language, plus proficiency with PyTorch, TensorFlow, or similar deep learning frameworks
- Experience with recommender systems and text-centric AI/ML including LLMs, document classification, and search
- Demonstrated ability to design user-facing ML features with cross-functional partners
- Commitment to inclusivity and fostering an open, respectful work environment
- Preferred: Technical leadership experience and/or a Ph.D. in Machine Learning or Computer Science
How to Apply
To apply for this position, follow the link below to the official Apple job posting. Make sure your resume is up to date and reflects your most relevant ML experience before submitting.
Share This Opportunity
Know someone who might be interested? Share this job posting and help them join Apple in Toronto.
Job Summary & Tips for Applying
Quick Summary & What to Highlight: This Staff Machine Learning Engineer role at Apple in Toronto is ideal for candidates who excel in machine learning model development, NLP and text-centric AI, and scalable system design. On your resume, emphasize experience shipping production ML models, work with recommender systems, and hands-on proficiency with deep learning frameworks like PyTorch or TensorFlow. If you’ve previously worked in media, content platforms, or search and ranking systems, highlight specific achievements and the scale at which your work operated.
Resume & Application Tips: Before applying, tailor your resume to reflect the language in the job description. Include keywords like machine learning, named entity recognition, recommender systems, LLMs, and scalable ML services. Quantify your contributions where possible — for example, “reduced duplicate content by 30% using ML classification” or “shipped ranking model serving 10M+ daily users.” A brief cover letter that speaks to your interest in Apple’s approach to privacy-conscious, editorially-guided ML can set your application apart. Proofread everything carefully and ensure your contact details are current.
Interview Preparation: Research Apple‘s values around user privacy, quality curation, and responsible AI before your interview. Prepare concrete examples using the STAR method (Situation, Task, Action, Result) that demonstrate your experience with shipping ML models at scale, cross-functional collaboration, and technical problem-solving. Be ready to discuss scenarios involving trade-offs in model accuracy vs. latency, approaches to text classification or entity recognition, and your leadership or mentorship experience. Dress professionally, arrive early if in-person, and bring copies of your resume. Follow up with a thank-you email within 24 hours of your interview.