Software Engineer – Applications

Apple • Austin, TX

7h • Onsite

About The Position

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.

Requirements

• Master’s degree or foreign equivalent in Biomedical Informatics, Computer Science, or related field.
• 5 years of experience in the job offered or related occupations.
• 5 years of experience applying natural language processing, machine learning, and deep learning algorithms to build and optimize intelligent systems.
• 5 years of experience utilizing Python and 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.
• 5 years of experience 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.
• 5 years of experience using SQL and relational databases (MS SQL Server) for data analysis, query optimization, and managing large-scale datasets.
• 5 years of experience 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 using TensorFlow Serving, NVIDIA Triton Inference Server, and BentoML; computer vision libraries and models to develop, deploy, and scale machine learning models and systems.
• 3 years of experience 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.
• 3 years of experience 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.
• 3 years of experience 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).
• 3 years of experience 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).
• 3 years of experience 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.

Responsibilities

• Drive, design, and implement new features for the platform.
• Ensure the platform can satisfy the growing demand for AI model serving.
• Stay on top of ML space developments and incorporate them into the platform.
• Work closely with ML engineers and data scientists to ensure efficient platform utilization.
• Collaborate with infrastructure and DevOps engineers to ensure platform scalability and reliability.
• Help shape the future and roadmap of the platform.
• Drive strong engineering excellence and culture.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior