Job description

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. 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. 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.

Requirements

Must-haves

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.
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
Deep, hands-on expertise in foundation model inference engines, with a proven

Nice-to-haves

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Application prep

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Keywords to mirror

llmmachine learningmlkubernetesdockerawsgcpazure

Platform: apple
Effort: medium

Benefits

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Vision