AIML - Distinguished Engineer, Foundation Model

Cupertino, California, United States Machine Learning and AI

Apple is revolutionizing artificial intelligence by developing sophisticatedfoundation models that power intelligent features across our product ecosystem.

We are seeking a Distinguished Engineer to set the technical direction for the systems that power our foundation model training — with an initial focus on theinference engine that the foundation model team relies on for training, model evaluation, and other needs in the model development loop.

This is a senior individual-contributor leadership role. You will be one of themost senior technical voices for foundation model systems at Apple: defining the vision, driving execution across many teams, and raising the bar for engineering excellence. The role starts with the inference engine, but weexpect you to move fluidly into adjacent training systems areas as the needs ofthe foundation model program evolve.

engineered specifically for Apple silicon and for experiences that are private,personal, and deeply integrated into the OS. Behind that modeling work sits ademanding systems layer, and the inference engine is at its center.

Our inference engine is used by the foundation model team throughout the model development lifecycle: generating and processing data and running rollouts for training, powering large-scale model evaluation, and serving as an LLM judgethat scores and compares model outputs. These workloads are high throughput,bursty, and tightly coupled to research iteration — the speed, efficiency, and reliability of the engine directly set the pace at which the team can train and improve models.

As a Distinguished Engineer, you will own the technical strategy for thisinference engine and the broader systems that support it. You will partner closely with modeling and research teams to bring new capabilities into the development loop, work across many internal teams with very different requirements, and lead a diverse set of engineers in turning an ambitiousvision into shipped milestones. While inference is the initial focus, you willalso help shape adjacent areas — training infrastructure, data systems, an devaluation.

If you are drawn to hard systems problems where the research andthe infrastructure are inseparable, this is the role.

• Set and drive the technical vision and roadmap for the foundation model
• team's inference engine and the systems around it, used for training,
• evaluation, and LLM-as-judge workloads.
• Lead deep work on inference performance, efficiency, and reliability:
• throughput and latency optimization, batching and scheduling, quantization,
• speculative decoding, KV-cache management, memory and compute efficiency, and
• hardware-aware optimization.
• Architect inference systems that support a wide range of internal use cases —
• data generation and rollouts for training, offline and large-scale evaluation,
• and judge/reward scoring — across text, image, speech, and multi-modal models,
• each with distinct throughput, cost, and quality constraints.
• Extend your impact into adjacent systems areas — training infrastructure, data
• pipelines, and evaluation harnesses.
• Partner with many teams that depend on the engine, translating their diverse
• needs into a coherent platform, clear interfaces, and a prioritized roadmap.
• Work closely with ML researchers and modeling teams to co-design models and
• systems, and to bring state-of-the-art techniques from prototype into the
• Lead a diverse set of engineers across teams in setting direction and
• executing against it; align stakeholders, resolve technical trade-offs, and
• make the calls that keep large efforts moving.
• Drive prioritization and milestone delivery across competing demands, balancing
• near‑term research needs against long‑term…