AIML - Distinguished Engineer, Foundation Model

Apple is revolutionizing artificial intelligence by developing sophisticated

foundation 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 the

inference 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 the

most 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 we

expect you to move fluidly into adjacent training systems areas as the needs of

the foundation model program evolve.

Description

We build the frontier foundation models behind Apple's intelligent experiences,

engineered specifically for Apple silicon and for experiences that are private,

personal, and deeply integrated into the OS. Behind that modeling work sits a

demanding 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 judge

that 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 this

inference 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 ambitious

vision into shipped milestones. While inference is the initial focus, you will

also help shape adjacent areas - training infrastructure, data systems, and

evaluation. If you are drawn to hard systems problems where the research and

the infrastructure are inseparable, this is the role.

Minimum Qualifications

MS or PhD in Computer Science, Machine Learning, or related technical field, or equivalent industry experience.

15+ years of experience building large-scale ML or distributed systems, with a track record of technical leadership and industry-wide or company-wide impact.

Deep, hands-on expertise in foundation model inference engines, with a proven record of improving performance, efficiency, and reliability at scale.

Deep experience supporting a diverse set of foundation model inference use cases, each with different throughput, latency, cost, and quality constraints.

Breadth beyond inference - the ability to contribute in adjacent systems areas such as training infrastructure, data systems, or evaluation.

Deep understanding of GPU/TPU/accelerator architecture, distributed systems, and model optimization (quantization, distillation, compilation, serving).

Proficiency with ML frameworks such as JAX, PyTorch, and with inference/serving stacks.

Proven experience leading a diverse set of engineers in setting vision and driving execution, including prioritization for milestone deliveries.

Demonstrated experience mentoring junior and senior engineers.

Demonstrated experience partnering with ML researchers and modeling teams to productionize research.

Preferred Qualifications

Experience building or leading inference systems for large language models and multi-modal foundation models at scale.

Experience with inference in training, evaluation, or reinforcement-learning loops (e.g., large-scale rollouts, offline eval, or LLM-as-judge / reward scoring).

Familiarity with Kubernetes, Docker, and cloud platforms (AWS, Google Cloud Platform, Azure), and with distributed computing frameworks.

History of defining technical strategy that shaped an organization's or the industry's direction.