Software Engineer, PAR Regulatory Readiness

Meta; Menlo Park (CA); On-site; USD 180,000 - 240,000; Full time

Job summary

Meta is seeking a Research Engineer specializing in AI to define and drive the technical direction of AI systems powering products used by billions. You will architect and deliver large-scale AI infrastructure, foundational model capabilities, and intelligent systems spanning Meta's apps and platforms.

You will identify hard problems at the intersection of AI research and production engineering, translate advances into reliable, high-impact systems, and set technical standards for building and

Qualifications

• Bachelor's degree in Computer Science, Computer Engineering, or related field.
• 3+ years of software engineering experience with a focus on AI, machine learning systems, or large-scale distributed systems.
• Experience architecting and owning production AI or ML platforms at scale, including reliability and performance.
• Experience defining technical strategy and driving execution across multiple engineering teams.
• Experience identifying and resolving systemic issues in AI pipelines across model, data, and infra.
• Experience establishing engineering standards, architectural patterns, and verification practices for AI development.
• Experience building or contributing to large-scale foundation model training infrastructure.
• Experience with AI inference optimization techniques and hardware-aware development.
• Collaboration with policy, privacy, or integrity teams for responsible AI deployment.

Responsibilities

• Identify and solve the most complex AI systems challenges across the organization, including model training, inference optimization, and deployment pipelines.
• Architect extensible, reliable foundations for AI infrastructure enabling teams to build ML models at scale.
• Define technical strategy and roadmap for AI platform capabilities with cross-functional alignment.
• Drive cross-functional execution of multi-year AI initiatives with measurable progress.
• Establish testing frameworks and verification standards to prevent model correctness issues in production.
• Identify and resolve systemic performance bottlenecks across the AI stack from data to real-time inference.
• Collaborate with AI research teams to translate advances into production systems delivering measurable improvements.
• Mentor engineers on AI systems design, debugging techniques, and engineering best practices.
• Evaluate emerging AI technologies to assess applicability and risk to Meta's AI strategy.
• Collaborate with legal, policy, and compliance teams to ensure privacy and integrity standards.

Skills

AI systems design

Distributed systems

Technical leadership

Research to production

Model training infra

Inference optimization

Cross-functional

Education

Bachelor's degree in CS or related field

Tools

TensorFlow

PyTorch

Distributed training

Job description

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

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 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