Senior AI Engineer - Services Special Projects

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.

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.

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)