AIML - Sr. LLM Inference Quality Engineer

Apple is building the next generation of AI data center infrastructure to power large language model (LLM) services at scale. We are looking for a senior engineer to own the correctness, consistency, and stability of LLM inference in production.

You will design and build the automation frameworks, evaluation methodology, and qualification strategy that give us confidence in every model, prompt, serving stack, and infrastructure change before it reaches our users. Your work is the safety net that lets Apple ship frontier AI quickly, without sacrificing quality, safety, or trust.

In this role, you will lead the qualification of an LLM inference, output-correctness, automation framework for large-scale AI data center deployment and platform validation.

You will architect qualification systems for generative AI, including agentic workflows and tool-using models, running at data center scale, validating that outputs remain correct, consistent, and stable as models, weights, prompts, serving runtimes, quantization schemes, and accelerator hardware change underneath them.

This is a hands-on AI systems engineering role at the intersection of machine learning, distributed systems, and quality engineering. You will write production-quality software in Python, C++, and/or Swift, apply statistical and ML-evaluation methods to validate both data and model behavior, and build AI-assisted QA tooling that makes testing itself smarter and faster.

You will partner closely with ML engineers, inference/performance engineers, distributed systems engineers, and platform teams, and you will be a trusted Quality voice on what “correct” and “stable” mean for LLM inference at scale, including the inherently non-deterministic, probabilistic nature of generative output.

You should be comfortable operating in ambiguity, defining quality bars where none exist yet, and delivering rigorous results on tight schedules.

5+ years of experience with hardware and/or software development lifecycle processes. 5+ years of experience in one or more compiled languages (e.g., C, C++, Objective-C/Swift). Proficiency in one or more scripting languages (e.g., Python, Go, or JavaScript).

Experience using AI/LLMs to improve testing itself — test-case generation, defect detection, or reducing manual QA effort. Familiarity with LLM serving and inference optimization (batching, KV-cache, quantization, speculative decoding, distributed/multi-accelerator serving) and GPU-accelerated software.

Experience with CI/CD, Kubernetes, containers, and cloud-native systems, and integrating automated evals into release pipelines. Experience with machine learning and LLMs, their common practical applications, and commonly used frameworks (e.g., PyTorch, Hugging Face Transformers, or comparable inference/serving stacks).

Experience with observability and distributed tracing for ML systems (e.g., Splunk, Prometheus, Grafana, LangSmith, or similar) and monitoring for model drift. B.S., M.S., or Ph.D. in Computer Engineering, Electrical Engineering, Computer Science, or equivalent experience.

5+ years using one or more of the following scripting languages: Python, Go, or JavaScript 5+ years of experience working with and building operating systems 2+ years of experience in LLM testing/development.