Senior AI Engineer - Services Special Projects

Apple

11d

About The Position

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. 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.

Requirements

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.
Hands-on experience deploying, serving, and optimizing LLMs or ML models in production.
Experience with inference runtimes/compilers (ONNX Runtime, TensorRT/TensorRT-LLM).
Experience with serving frameworks (Triton, vLLM, SGLang, TorchServe, or similar).
Experience 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.
Experience with backend service frameworks (e.g., Flask, FastAPI) for building ML/LLM services, pipelines, and tooling.
Working proficiency in Java or Scala.
Experience with distributed systems.
Experience with cloud platforms (e.g., AWS).
Experience with container orchestration (Kubernetes).
Experience with CI/CD pipelines.
Experience building Data Pipelines on Spark using Airflow.
Experience with ML lifecycle management and versioning practices (experiment tracking, model registry, deployment automation, dataset/model versioning tools like DVC, MLflow, Weights & Biases, Delta Lake).
Experience with workflow orchestration platforms (Airflow).
Excellent communication skills and a collaborative, team-oriented mindset.

Nice To Haves

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.
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).

Responsibilities

Build, deploy, optimize, and operationalize Small and Large Language Model (LLM)-based applications.
Own the infrastructure and tooling for LLM-powered features, including CI/CD pipelines and serving infrastructure.
Manage observability, versioning, and governance for production models.
Work across the full model lifecycle: experimentation, fine-tuning, deployment, monitoring, and retirement.
Build pipelines for ingesting and enriching multimodal data through feature stores and lineage-tracked storage.
Deploy and operate services on cloud-native infrastructure like Kubernetes.
Optimize models for production using techniques like quantization, distillation, and compilation.
Implement governance workflows, approval gates, and audit trails for model compliance.
Build safety guardrails for model outputs to prevent misuse and ensure user privacy.
Mentor other engineers and set technical standards for the team.