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Open nowFirst seen 43 hours ago

Software Engineer, PAR Regulatory Readiness

Meta989 open roles

Where
Menlo Park, CA
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Your applicationOpen nowSoftware Engineer, PAR Regulatory ReadinessMeta · Menlo Park, CA

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The clock on this job

Early applications get read.

7.8% of postings close within 7 days. Measured by our own scanner across the market. Meta postings stay open a median of 35 days.

Share of postings closed within
  1. 1.6%1 day
  2. 3.4%3 days
  3. 7.8%7 days
  4. 14.3%14 days
  5. 33.7%30 days
This job: first seen 43 hours ago

Meta median: 35 days open

The posting

Meta is seeking a Research Engineer specializing in AI to help define and drive the technical direction of AI systems that power products used by billions of people worldwide. In this role, you will architect and deliver large-scale AI infrastructure, foundational model capabilities, and intelligent systems that span Meta's family of apps and platforms while preparing for upcoming AI regulations. You will identify the hardest unsolved 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 the most complex AI systems challenges across the organization, including problems that span model training, inference optimization, and large-scale deployment pipelines
  • Architect extensible, reliable foundations for AI infrastructure that enable multiple teams to build and iterate on machine learning models at scale
  • Define technical strategy and roadmap for AI platform capabilities, gaining alignment across engineering, research, and product organizations
  • Drive cross-functional execution of multi-year AI initiatives, establishing metrics that connect technical progress to organization-level 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 performance 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 theoretical advances in machine learning into production systems that deliver measurable improvements to Meta's products
  • Mentor engineers across the organization on AI systems design, debugging techniques, and engineering best practices, serving as a sought-after technical advisor
  • Evaluate emerging AI technologies, frameworks, and industry trends to assess their applicability and risk to Meta's AI strategy and competitive position
  • Collaborate with legal, policy, and compliance teams to ensure AI systems meet privacy, security, and integrity standards across all deployment contexts

Minimum Qualifications

  • Bachelor's degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience
  • 3+ years of software engineering experience with a focus on AI, machine learning systems, or large-scale distributed systems that support model training and inference
  • Experience architecting and owning production AI or machine learning platforms at scale, including end-to-end responsibility for reliability, performance, and evolution of those systems
  • Experience defining technical strategy and driving execution across multiple engineering teams, including influencing roadmap priorities and gaining cross-functional alignment
  • Experience identifying and resolving systemic issues in AI pipelines, including debugging complex failures that span model behavior, data quality, and infrastructure layers
  • Experience establishing engineering standards, architectural patterns, and verification practices that improve the quality and velocity of AI development across an organization

Preferred Qualifications

  • Track record of publishing or productionizing novel approaches in machine learning, systems for ML, or AI safety and reliability at scale
  • Experience building or significantly contributing to large-scale foundation model training infrastructure, including distributed training frameworks, mixed-precision optimization, or model parallelism strategies
  • Experience with AI inference optimization techniques such as quantization, distillation, speculative decoding, or hardware-aware kernel development for accelerators
  • Experience collaborating with AI policy, privacy, or integrity teams to design technical safeguards that address responsible AI deployment requirements

US: $219,000/year to $301,000/year + bonus + equity

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