Job Description

Join our innovative AI/ML - Machine Translation team and be at the forefront of revolutionizing human-computer interaction. As a Senior Software Engineer, you will focus on developing and enhancing our Machine Learning Automation and Tooling. You will work closely with cross-functional teams to build scalable, robust, and efficient model automation pipelines that power our language technologies.

Description

Responsibilities: Design and Develop Model Automation Pipelines: Lead the design and implementation of highly scalable and efficient model automation pipelines for machine translation. ML Lifecycle Management: Implement and manage the ML lifecycle, including model deployment, monitoring, and retraining. Big Data Processing: Utilize expertise in big data technologies such as Spark and MapReduce to handle large quantities of data efficiently. Deep Learning Integration: Integrate state-of-the-art deep learning methods into our existing pipelines to solve real-world machine translation problems. ML CI/CD Pipelines: Set up and maintain continuous integration/continuous deployment (CI/CD) pipelines for machine learning models. Collaboration: Work closely with researchers, ML engineers, and other engineers to deliver high-quality products.

Minimum Qualifications

• Bachelor’s or Master’s degree in Computer Science or a related field.
• 5+ years of experience in software engineering, with a focus on machine learning or natural language processing.
• Proficiency in Python programming language.
• Experience with big data technologies like Spark or Hadoop.
• Knowledge of model management systems (e.g., MLflow, Kubeflow).
• Strong problem-solving and analytical skills.
• Excellent communication and collaboration abilities.

Key Qualifications

Preferred Qualifications

• Knowledge of machine translation (MT), natural language processing (NLP), or automatic speech recognition (ASR).
• Experience with ML frameworks such as PyTorch or TensorFlow.
• Experience with cloud computing platforms (AWS, Azure, Google Cloud).

Education & Experience

Additional Requirements

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 $135,400 and $250,600, 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. Learn more about your EEO rights as an applicant.

Responsibilities: Design and Develop Model Automation Pipelines: Lead the design and implementation of highly scalable and efficient model automation pipelines for machine translation. ML Lifecycle Management: Implement and manage the ML lifecycle, including model deployment, monitoring, and retraining. Big Data Processing: Utilize expertise in big data technologies such as Spark and MapReduce to handle large quantities of data efficiently. Deep Learning Integration: Integrate state-of-the-art deep learning methods into our existing pipelines to solve real-world machine translation problems. ML CI/CD Pipelines: Set up and maintain continuous integration/continuous deployment (CI/CD) pipelines for machine learning models. Collaboration: Work closely with researchers, ML engineers, and other engineers to deliver high-quality products.

Job Tags

Relocation,

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