About this role
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.
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, GCP, Azure),
and with distributed computing frameworks.
History of defining technical strategy that shaped an organization's or the
industry's direction.