Machine Learning Operations Engineer
Location
Cupertino, CA | United States
Job description
The people here at Apple don't just create products - they create the kind of wonder that's revolutionized entire industries. It's the diversity of those people and their ideas that inspires the innovation that runs through everything we do, from amazing technology to industry-leading environmental efforts. Join Apple, and help us leave the world better than we found it!
Apple is seeking a highly skilled and proactive Machine Learning Operations Engineer to join our worldwide sales team, Data Solutions & Initiatives (DSI). DSI is a product strategy and engineering team that works closely with business development and sales finance. As an ML Ops Engineer, you will play a key role you in deploying, managing, and optimizing machine learning models in our production environment. You will collaborate with data scientists, ML engineers, software engineers, and other cross-functional teams to ensure the seamless integration of machine learning solutions into our systems. These models will drive Apple critical financial planning and business activities.
If you are passionate about deploying cutting-edge machine learning models into real-world applications and enjoy working in a collaborative environment, we invite you to apply for this exciting opportunity.
Key Qualifications
- 5+ years of machine learning industry experience
- Strong coding skills and experience with data structures and algorithms such as LLM, NLP, etc...
- Experience designing and building scalable distributed services
- Strong proficiency with AWS Services such as Amazon S3 EC2 EKS / Kubernetes
- Experience with version control systems (e.g., Git) and infrastructure as code (e.g., Terraform, Ansible).
- Experience with machine learning algorithms and tools
- Experience with security practices in machine learning
- Experience with data management and processing pipelines
- Familiarity with continuous integration and continuous deployment (CI/CD) pipelines.
- Excellent scripting and programming skills (Python, Shell)
- Strong problem-solving and troubleshooting skills
- Excellent interpersonal skills able to work independently as well as in a team
Description
Your responsibilities will include:
Infrastructure Design and Deployment:
* Architect and maintain scalable, reliable, and efficient infrastructure for deploying and running machine learning models in production environments.
* Collaborate with DevOps and IT teams to ensure the smooth integration of ML systems with existing infrastructure.
Automation and CI/CD:
* Develop and implement automated processes for deploying, monitoring, and scaling machine learning models.
* Work closely with data scientists and software engineers to establish and maintain continuous integration and continuous deployment (CI/CD) pipelines for machine learning workflows.
Monitoring and Performance Optimization:
* Implement robust monitoring solutions to track the performance of deployed models, infrastructure and overall system health.
* Proactively identify and address issues related to model drift, data quality, and system reliability.
Collaboration and Communication:
* Collaborate with cross-functional teams, including program managers, data scientists, software developers, domain experts, and other stakeholders to facilitate the deployment and operationalization of machine learning models.
* Clearly communicate technical concepts to non-technical team members and stakeholders.
Security and Compliance:
* Implement security best practices for machine learning systems, ensuring the protection of sensitive data.
* Ensure compliance with relevant regulations and industry standards.
Documentation and Knowledge Sharing:
* Create and maintain comprehensive documentation for deployment processes, system architecture, and best practices
* Facilitate knowledge sharing across teams to enhance the overall understanding of machine learning operations.
Education & Experience
MS or PhD in Computer Science or relevant field
Additional Requirements
- Certification in cloud platforms (AWS Certified DevOps Engineer, Google Cloud Professional DevOps Engineer, etc.) preferred
Pay & Benefits
- At Apple, base pay is one part of our total compensation package and is determined within a range. This provides the opportunity to progress as you grow and develop within a role. The base pay range for this role is between $170,700.00 and $256,500.00, and your base pay will depend on your skills, qualifications, experience, and location. Apple employees also have the opportunity to become an Apple shareholder through participation in Apple’s discretionary employee stock programs. Apple employees are eligible for discretionary restricted stock unit awards, and can purchase Apple stock at a discount if voluntarily participating in Apple’s Employee Stock Purchase Plan. You’ll also receive benefits including: Comprehensive medical and dental coverage, retirement benefits, a range of discounted products and free services, and for formal education related to advancing your career at Apple, reimbursement for certain educational expenses — including tuition. Additionally, this role might be eligible for discretionary bonuses or commission payments as well as relocation. Learn more about Apple Benefits. Note: Apple benefit, compensation and employee stock programs are subject to eligibility requirements and other terms of the applicable plan or program. Apple is an equal opportunity employer that is committed to inclusion and diversity. We take affirmative action to ensure equal opportunity for all applicants without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, Veteran status, or other legally protected characteristics.
Your responsibilities will include:
Infrastructure Design and Deployment:
* Architect and maintain scalable, reliable, and efficient infrastructure for deploying and running machine learning models in production environments.
* Collaborate with DevOps and IT teams to ensure the smooth integration of ML systems with existing infrastructure.
Automation and CI/CD:
* Develop and implement automated processes for deploying, monitoring, and scaling machine learning models.
* Work closely with data scientists and software engineers to establish and maintain continuous integration and continuous deployment (CI/CD) pipelines for machine learning workflows.
Monitoring and Performance Optimization:
* Implement robust monitoring solutions to track the performance of deployed models, infrastructure and overall system health.
* Proactively identify and address issues related to model drift, data quality, and system reliability.
Collaboration and Communication:
* Collaborate with cross-functional teams, including program managers, data scientists, software developers, domain experts, and other stakeholders to facilitate the deployment and operationalization of machine learning models.
* Clearly communicate technical concepts to non-technical team members and stakeholders.
Security and Compliance:
* Implement security best practices for machine learning systems, ensuring the protection of sensitive data.
* Ensure compliance with relevant regulations and industry standards.
Documentation and Knowledge Sharing:
* Create and maintain comprehensive documentation for deployment processes, system architecture, and best practices
* Facilitate knowledge sharing across teams to enhance the overall understanding of machine learning operations. MS or PhD in Computer Science or relevant field
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