Cookies & analytics consent
We serve candidates globally, so we only activate Google Tag Manager and other analytics after you opt in. This keeps us aligned with GDPR/UK DPA, ePrivacy, LGPD, and similar rules. Essential features still run without analytics cookies.
Read how we use data in our Privacy Policy and Terms of Service.
🤖 15+ AI Agents working for you. Find jobs, score and update resumes, cover letter, interview questions, missing keywords, and lots more.

Apple • Cupertino, California, United States
Role & seniority: Senior Automation Engineer (ML/AI validation and data pipelines)
Stack/tools: Python; test automation/infrastructure design; ML frameworks (PyTorch, TensorFlow); data tools (Jupyter, Pandas, NumPy, Matplotlib); data visualization/reporting (Tableau, Splunk); large datasets; automation frameworks and validation pipelines
Design, develop, and maintain data collection and evaluation pipelines for ML systems
Build automation frameworks and validation pipelines; design experiments; analyze/visualize data; enable root-cause analysis of failures
Collaborate with algorithm teams, own issues, and continuously improve tests/processes toward automation and reliability
Must-have skills: Bachelor’s degree in CS/ML or related field; 3+ years hands-on ML/AI validation or development; Python proficiency; strong test automation and infrastructure design; experience with large datasets and computer vision; effective communication with partner teams
Nice-to-haves: 5+ years ML industry experience; debugging/improving deployed models; experience with analytics tools (Jupyter, Pandas, NumPy, Matplotlib); training models (PyTorch, TensorFlow); data analysis/visualization/reporting (Tableau, Splunk)
Location & work type: Location not specified; Full-time expected
We are the Product Systems Quality team and we are looking for a highly motivated and experienced automation engineer with a passion for delivering robust, inclusive, and state-of-the-art Computer Vision and Machine Learning algorithms in Apple’s next generation of products. You'll enjoy working on a team of quality engineers with a diverse group of backgrounds as we refine the model pipelines that power Apple’s trademark simple and intuitive user experience. Come be a part of our team and use both creativity and technical expertise to bring experiences to life that our customers love!
DESCRIPTION
We are seeking an experienced Automation Engineer to lead the design and implementation of data collection and evaluation pipelines for Apple’s ML systems. In this role, you will design, develop and maintain automation frameworks and validation pipelines for machine learning models. Your work will require close partnership with algorithm development teams to build tools for analyzing and visualizing data, evaluate and represent the true customer experience, design and implement experiments for engineering studies. You will develop and execute validation strategies to ensure the models meet accuracy & reliability requirements as well as create visualization for comparing model validation results across configurations to enable root cause analysis for failures. You’ll have opportunity to take ownership of issues, drive them to resolution and continuously improve tests and processes with a drive towards innovation and automation. You'll be working through every step of the product development cycle and will help make Apple products more reliable, flexible, and easy to use.
MINIMUM QUALIFICATIONS
Bachelor's degree or equivalent in Computer Science, Machine Learning or related field A minimum of 3 years of hands-on industry experience developing or validating ML/AI systems Proficiency in Python with strong background in test automation and test infrastructure design Experience in working with large datasets for testing products utilizing computer vision, machine learning Ability to communicate effectively and collaborate with partner teams
PREFERRED QUALIFICATIONS
5 or more years of ML industry experience, including time spent debugging or improving deployed models Experience with analytical tools such as Jupyter, Pandas, NumPy, Matplotlib Experience in training models using frameworks like PyTorch, TensorFlow Data analysis, visualization, and reporting experience with tools such as Tableau, or Splunk