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Sr. Software Engineer, Continuous Integration

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Apple

Senior Machine Learning Engineer- Worldwide Product Marketing

Cupertino, CA$216,200 – $324,800Posted 28 days ago

Apply on company site View full job

Why you should apply for a job to Apple:

Ranked as one of the Best Technology Companies in 2020
3.7/5 in overall job satisfaction
3.1/5 in supportive management
66% say women are treated fairly and equally to men
65% would recommend this company to other women
66% say the CEO supports gender diversity

Ratings are based on anonymous reviews by Fairygodboss members. See all reviews ›

As an MLOps Engineer, you will be the backbone of our machine learning infrastructure, ensuring that AI/ML systems are reliable, scalable, and continuously improving in production. You will bridge the gap between data science and engineering, driving operational excellence across the full ML lifecycle.

Description

The MLOps Engineer will drive end-to-end quality initiatives across data ingestion, model training, deployment pipelines, and MLOps tooling. This hire will build, deploy, and optimize AI/ML based applications with a strong emphasis on scalable, and production-ready systems. You will establish standard methodologies for model integration, deployment, and monitoring using CI/CD principles.","responsibilities":"Explore, design, and implement advanced ML infrastructure frameworks and tools to accelerate model development and delivery.

Champion model observability, incident response, prompt versioning, and feedback loops to ensure continuous model health and performance.

Design and maintain automated pipelines for model training, evaluation, versioning, and deployment.

Partner closely with ML Engineers and Data Scientists to define metrics, gather requirements, and deliver impactful solutions.

Enforce model governance, validation standards, and best practices across teams to ensure reproducibility and compliance.

Identify and resolve bottlenecks in ML workflows, improving system reliability, latency, and throughput at scale.

Leverage AI coding assistants and LLM-based tools (e.g., Claude, Gemini, GitHub Copilot) to accelerate development, automate repetitive tasks, and improve engineering productivity across ML workflows.

Use LLM-based tools to assist in drafting technical documentation, runbooks, and incident post-mortems, reducing operational overhead.

Apply LLM assistants to support code reviews, test generation, and pipeline debugging to improve overall code quality and team velocity.

Preferred Qualifications

10 years of related experience building high-throughput, scalable applications or machine learning models in a production environment.

Familiarity with model monitoring, drift detection, and observability practices in production environments.

Excellent cross-functional communication skills with the ability to collaborate effectively across engineering and data science teams.

Comfort using LLM-based tools such as Claude, Gemini, or ChatGPT to assist with code generation, documentation, debugging, and workflow automation.

Demonstrated ability to critically evaluate and validate LLM-generated outputs, ensuring accuracy and reliability before applying them in production contexts.

Experience incorporating AI-assisted tools into day-to-day engineering workflows, with an understanding of their limitations and appropriate use cases.

Minimum Qualifications

8 years in software engineering with demonstrated experience in large-scale software system design and implementation.

Bachelor's Degree in Software Engineering, Computer Science, Statistics, Data Mining, Machine Learning, Operations Research, or related field.

Proven track record of shipping and maintaining production-grade ML systems end-to-end.

Strong experience with distributed systems, databases (SQL/NoSQL), cloud platforms (AWS, Azure, or GCP), and container orchestration tools such as Kubernetes.

Hands-on experience with MLOps tooling and platforms such as Ray, MLflow, Kubeflow, SageMaker, Vertex AI, or similar.

Proficiency in Python and familiarity with ML frameworks such as TensorFlow, PyTorch, or scikit-learn.

Experience building and managing CI/CD pipelines for ML workflows using tools such as Jenkins, GitHub Actions, or ArgoCD.

Strong understanding of data pipeline orchestration tools such as Airflow, Prefect, or similar.

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 $216,200 and $324,800, 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.

About Apple

Working at Apple means doing more than you ever thought possible and having more impact than you ever imagined.

Size: 10,000+ employees
Industry: Technology: Manufacturing

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