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Jobfoundry
Apple

Machine Learning Engineer, Data Solutions & Initiatives

Apple
TechnologySingaporeFull-time4 months ago
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About the role

AI summarised

Apple is seeking a Senior Machine Learning Engineer to join its worldwide sales team, Data Solutions & Initiatives (DSI). The role involves building AI/ML products for financial planning and business activities, owning the entire ML pipeline from design to deployment, and collaborating with data engineers, software engineers, and MLOps.

TechnologyFull-timeMachine Learning and AI

Key Responsibilities

  • Build a suite of AI / Machine Learning products that deliver measurable business value for Sales and Finance Business community
  • Work closely with Data Engineers, Software Engineers and MLOps
  • Own the entire AI/ML pipelines from requirement gathering to deployment and monitoring, including design, experimentation, implementation, and testing
  • Deliver best in class and highly available AI/ML systems

Requirements

  • Bachelor's degree in Computer Science, Machine Learning, or related technical field
  • 5+ years of experience in Machine Learning Engineering, Software Engineering, or Data Science with focus on production ML systems
  • Strong proficiency in Python and SQL
  • Solid understanding of machine learning fundamentals including supervised and unsupervised learning algorithms
  • Experience building and deploying ML models in production environments
  • Familiarity with ML frameworks such as scikit-learn, PyTorch, OpenAI, Langchain/graph
  • Strong software engineering skills with ability to write clean, maintainable code
  • Experience with cloud platforms (AWS, GCP, or Azure) and basic cloud services
  • Excellent problem-solving and analytical skills
  • Strong written and verbal communication skills with ability to collaborate across technical and non-technical teams
  • MS or PhD in Computer Science, Machine Learning, or related technical field (preferred)
  • 7+ years of Machine Learning Engineering, Software Engineering, Data Science or related roles with focus on production ML systems (preferred)
  • Track record with agentic workflows, advanced RAG architectures, and LLM frameworks (OpenAI, Anthropic, LangChain, LlamaIndex) (preferred)
  • Expertise in prompt engineering, fine-tuning, LLM evaluation, and vector databases (ElasticSearch, Chroma) (preferred)
  • Deep expertise in ML libraries (scikit-learn, PyTorch, XGBoost, LightGBM) and lifecycle management tools (MLflow, W&B) (preferred)
  • Experience with API frameworks (FastAPI, Flask) and monitoring tools (Grafana, Prometheus, LangFuse) (preferred)
  • Advanced AWS experience (EKS, S3, Athena, Lambda, SQS, EventBridge) and container orchestration (Kubernetes, Docker) (preferred)
  • Proficiency with workflow orchestration (Airflow), streaming technologies (Kafka, Kinesis), and caching solutions (Redis) (preferred)