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Software Engineer, PAR Regulatory Readiness

Key requirements

Identify and solve complex AI systems challenges across model training, inference optimization, and large-scale deployment pipelines.
Architect extensible, reliable foundations for AI infrastructure enabling multiple teams to build and iterate on machine learning models at scale.
Define the technical strategy and roadmap for AI platform capabilities, gaining alignment across engineering, research, and product teams.
Drive cross-functional execution of multi-year AI initiatives, establishing metrics that connect technical progress to organizational priorities.
Establish invariants, testing frameworks, and verification standards that prevent entire categories of model correctness and reliability issues in production AI systems.

Key requirements

Identify and solve complex AI systems challenges across model training, inference optimization, and large-scale deployment pipelines.
Architect extensible, reliable foundations for AI infrastructure enabling multiple teams to build and iterate on machine learning models at scale.
Define the technical strategy and roadmap for AI platform capabilities, gaining alignment across engineering, research, and product teams.
Drive cross-functional execution of multi-year AI initiatives, establishing metrics that connect technical progress to organizational priorities.
Establish invariants, testing frameworks, and verification standards that prevent entire categories of model correctness and reliability issues in production AI systems.

Jobs at Meta

7 active roles from this employer in the JobMatcher catalog.

All company jobs

Jobs at Meta

7 active roles from this employer in the JobMatcher catalog.

All company jobs

About the role

Meta is seeking a Software Engineer specializing in AI to define and drive the technical direction of AI systems powering Meta's products, with a focus on PAR Regulatory Readiness. This on-site role in Menlo Park will architect and deliver large-scale AI infrastructure, foundational model capabilities, and intelligent systems while preparing for upcoming AI regulations. You will identify the hardest problems at the intersection of AI research and production engineering, translate cutting-edge advances into reliable, high-impact systems, and set the technical standard for how AI is built and deployed across the organization.

Responsibilities

Identify and solve complex AI systems challenges across model training, inference optimization, and large-scale deployment pipelines.
Architect extensible, reliable foundations for AI infrastructure enabling multiple teams to build and iterate on machine learning models at scale.
Define the technical strategy and roadmap for AI platform capabilities, gaining alignment across engineering, research, and product teams.
Drive cross-functional execution of multi-year AI initiatives, establishing metrics that connect technical progress to organizational priorities.
Establish invariants, testing frameworks, and verification standards that prevent entire categories of model correctness and reliability issues in production AI systems.
Identify and resolve systemic bottlenecks across the AI stack, from data ingestion and feature engineering through model serving and real-time inference.
Partner with AI research teams to translate t

About the role

Meta is seeking a Software Engineer specializing in AI to define and drive the technical direction of AI systems powering Meta's products, with a focus on PAR Regulatory Readiness. This on-site role in Menlo Park will architect and deliver large-scale AI infrastructure, foundational model capabilities, and intelligent systems while preparing for upcoming AI regulations. You will identify the hardest problems at the intersection of AI research and production engineering, translate cutting-edge advances into reliable, high-impact systems, and set the technical standard for how AI is built and deployed across the organization.

Responsibilities

Identify and solve complex AI systems challenges across model training, inference optimization, and large-scale deployment pipelines.
Architect extensible, reliable foundations for AI infrastructure enabling multiple teams to build and iterate on machine learning models at scale.
Define the technical strategy and roadmap for AI platform capabilities, gaining alignment across engineering, research, and product teams.
Drive cross-functional execution of multi-year AI initiatives, establishing metrics that connect technical progress to organizational priorities.
Establish invariants, testing frameworks, and verification standards that prevent entire categories of model correctness and reliability issues in production AI systems.
Identify and resolve systemic bottlenecks across the AI stack, from data ingestion and feature engineering through model serving and real-time inference.
Partner with AI research teams to translate t

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