Senior AI Engineer - Services, Special Projects

Summary

At Apple, great ideas turn into phenomenal products, services, and customer experiences at a pace few companies can match. We are seeking a highly experienced ML Engineer to build, deploy, optimize and operationalize Small and Large Language Model (LLM)-based applications, with a strong emphasis on MLOps/LLMOps and scalable production systems.

Minimum Qualifications

Master's degree in Computer Science, Engineering, or a related field 8+ years of experience in Machine learning and software engineering Proven track record of shipping production-grade ML/LLM systems Strong understanding of LLMs, fine-tuning, prompt engineering, and RAG patterns Experience building pipelines that process multimodal data (structured and image) and integrate ML model inference, including LLMs and embedding models, for data enrichment and transformation Hands-on experience deploying, serving, and optimizing LLMs or ML models in production, including inference runtimes/compilers (ONNX Runtime, TensorRT/TensorRT-LLM), serving frameworks (Triton, vLLM, SGLang, TorchServe, or similar), and tuning batching, KV-cache, and GPU utilization for low-latency, high-throughput inference Experience with vector search technologies (e.g., Pinecone, Milvus) and storing/serving embeddings (e.g., pgvector, FAISS) Experience with feature stores (e.g., Feast) and data lineage tracking Strong proficiency in Python, with solid software engineering fundamentals, including backend service frameworks (e.g., Flask, FastAPI), for building ML/LLM services, pipelines, and tooling Working proficiency in Java or Scala, sufficient to integrate with JVM-based data infrastructure (e.g., Spark, Flink, Kafka clients) and the broader services platform. Experience with distributed systems, cloud platforms (e.g., AWS), container orchestration (Kubernetes), CI/CD pipelines, and building Data Pipelines on Spark using Airflow Experience with ML lifecycle management and versioning practices, including experiment tracking, model registry, deployment automation, and dataset/model versioning tools (e.g., DVC, MLflow, Weights & Biases, Delta Lake) Experience with workflow orchestration platforms (Airflow) Excellent communication skills and a collaborative, team-oriented mindset

Preferred Qualifications

Ph.D. in Computer Science, Machine Learning, or a related field Experience with Go Solid understanding of machine learning algorithms, model evaluation metrics, and data processing pipelines Active participation in open-source projects related to AI/ML or backend development Familiarity with graph databases such as TigerGraph Experience defining SLAs, quality metrics, and observability standards for large-scale data platforms, with hands-on use of monitoring/alerting tooling (e.g., Prometheus/Grafana, Datadog, or OpenTelemetry-based tracing). Track record of mentoring engineers and influencing technical direction across a team or organization Working knowledge of data privacy principles and practices (e.g., data minimization, access controls, privacy-preserving measurement) and experience applying them to ML data pipelines Experience implementing model governance frameworks, including approval workflows, audit trails, and compliance controls Experience implementing safety guardrails for LLM-powered systems, including content moderation, prompt-injection defenses, and red-teaming or adversarial evaluation practices Hands-on experience with observability and evaluation tools for LLMs (e.g., LangSmith, Weights & Biases, MLflow)

Description

As an AI Engineer on our team, you will own the infrastructure and tooling that let LLM-powered features ship reliably at Apple scale: the CI/CD pipelines and serving infrastructure that get a model into production, and the observability, versioning, and governance that keep it trustworthy once it's there. You'll work across the full model lifecycle, from experimentation and fine-tuning through deployment, monitoring, and retirement. That ownership extends to the data feeding these systems and the infrastructure serving them. You'll build pipelines that ingest and enrich multimodal data through feature stores and lineage-tracked storage, deploy and operate services on cloud-native infrastructure such as Kubernetes, and expose them through well-modeled APIs. You'll also optimize models for production through quantization, distillation, and compilation, and implement the governance workflows, approval gates, and audit trails that keep every model compliant on its way into production. You'll also own the trust side of the system: building the safety guardrails that keep model outputs safe from misuse and treating user privacy as a design constraint rather than an afterthought. As a senior member of the team, you'll mentor other engineers and help set the technical standards the rest of the team builds against. This is a role for someone who's comfortable operating at the intersection of ML and distributed systems, as much at home tuning GPU utilization and KV-cache for low-latency inference as designing the versioning strategy that makes a rollback safe.

Responsibilities

Own the full model lifecycle: from experimentation and training through validation, deployment, monitoring, and retirement, ensuring reproducibility and governance at every stage. Fine-tune and tune models, including hyperparameters, adapters/LoRA, and distillation targets, to improve quality, task fit, and efficiency. Design and build scalable ML infrastructure and experimentation platforms, including web-based interfaces, dashboards, and backend services, that enable rapid model development, testing, and deployment at scale. Define and implement CI/CD methodologies for model integration, deployment, versioning, and monitoring, and build the production infrastructure, including cloud-native deployment (Kubernetes, AWS) and well-modeled RESTful/GraphQL APIs, that serves high-traffic LLM services reliably and cost-efficiently. Optimize models for production, including quantization, distillation, and compilation (e.g., ONNX, TensorRT), tuning for token throughput, latency, and cost targets. Drive model observability, incident response, and feedback loops to ensure continuous quality improvement across AI products, and own the SLAs that define acceptable service quality. Design and implement frameworks that measure operational quality, reliability, latency, token throughput, and cost efficiency of model serving infrastructure. Implement model governance workflows, including approval gates, audit trails, and compliance controls, for models moving into production. Treat privacy as a design constraint across the data and model pipeline, applying data minimization, access controls, and privacy-preserving techniques to any user data used in training, enrichment, or evaluation. Establish robust versioning strategies for datasets, model artifacts, prompts, and configurations to enable reproducibility, auditability, and safe rollbacks across environments. Mentor engineers, set technical standards for ML infrastructure and MLOps practice, and partner closely with data scientists, data engineers, frontend engineers, product managers, Trust & Safety, and Privacy Review to define metrics, gather requirements, and deliver impactful solutions.