Full Job Description
APPLE INC has the following available in Austin, Texas. Build the next generation of our platform enabling a new wave of Al and Mlpowered applications across hundreds of teams and thousands of models. Drive, design, and implement new features making sure the platform can satisfy the ever-growing demand for Al models serving and staying on top of the faster-than-ever ML space developments and incorporating those into the platform Work closely with ML engineers and data scientists to make sure they can utilize the platform as efficiently as possible. Get all the features they need, infrastructure and DevOps engineers make sure the platform can scale and reliably serving every customer, and many teams at Apple using the platform to instrument and incorporate ML and Al into the core of their products. Help shape the future and the roadmap of our platform, drive strong engineering excellence and culture, and see the effect of your work on Apple customers.
40 hours/week.
Master's degree or foreign equivalent in Biomedical Informatics, Computer Science, or related field and 5 years of experience in the job offered or related occupations.
5 years of experience in each of the following areas is required:
Applying natural language processing, machine learning, and deep learning algorithms to build and optimize intelligent systems.
Utilizing Python and the common Python data science packages: PyTorch, TensorFlow, Hugging Face Transformers, scikit-learn, spaCy, NLTK, Gensim, Pandas, NumPy, Matplotlib, Seaborn, Plotly, Bokeh, LightGBM, CatBoost, Dask, and Vaex.
Using statistical methods including Logistic Regression, Linear Regression, Generalized Linear Models, Bayesian models, T-tests, Analysis of Variance, and Mixed Models to solve diverse problems in experimental design, hypothesis testing, A/B testing, predictive modeling, and inference.
Using SQL and relational databases (MS SQL Server) for data analysis, query optimization, and managing large-scale datasets.
Using Data Visualization with Tableau to design dynamic dashboards and interactive reports to drive data-driven decision-making and provide actionable insights from complex datasets.
3 years of experience in each of the following areas is required:
Using TensorFlow Serving, NVIDIA Triton Inference Server, and BentoML; computer vision libraries and models to develop, deploy, and scale machine learning models and systems.
Applying MLOps best practices and ensuring production-grade operations such as containerization (Docker and Kubernetes), CI/CD pipelines (GitLab, GitHub, Azure DevOps, Jenkins), automation testing, and comprehensive observability (logging, monitoring, and alerting) using Azure Application Insights, Prometheus, and Grafana to ensure low latency and high throughput.
Training large-scale machine learning models using GPU acceleration and distributed training frameworks: Horovod, DeepSpeed, Distributed Data Parallel; leverage CUDA programming and JAX for computation, and optimize sparse operations with SciPy, while automating hyperparameter tuning pipelines leveraging Optuna and Ray Tune to optimize model performance.
Designing high-scale, highly available distributed ML systems and applications with Python, Java, and Scala, implementing them in code with a focus on distributed messaging, storage, and compute using a variety of Azure and AWS cloud services, relational databases (Postgres), NoSQL databases (MongoDB, Azure Cosmos, Cassandra, Elasticsearch), graph databases (Neo4j), data warehouses (Teradata and Greenplum), vector databases (PostgreSQL PGVector, FAISS), and serverless platforms (Azure Functions).
Designing, implementing, and managing batch and real-time data processing pipelines in production using scalable data lakes and warehouses (Azure Blob Storage, Azure Data Lake Storage Gen2, Databricks, Delta lakes), cloud messaging services (Azure Event Hubs and Azure Service Bus), data processing frameworks (Spark, Azure Databricks), and orchestration platforms (Apache Airflow and Azure Data Factory).
Designing, developing, deploying, and producing Generative AI applications using large language models (LLMs), Retrieval-Augmented Generation (RAG) workflow and Agentic RAG architectures applying LLMOps best practices for scalable AI pipelines orchestration using MLflow, Weights & Biases, LangChain, LlamaIndex, and Hugging Face Transformers.
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