软件工程师，PAR监管准备

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

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

Meta builds technologies that help people connect, find communities, and grow businesses.

When Facebook launched in 2004, it changed the way people connect.

Apps like Messenger, Instagram and WhatsApp further empowered billions around the world.

Now, Meta is moving beyond 2D screens toward immersive experiences like augmented and virtual reality to help build the next evolution in social technology.

People who choose to build their careers by building with us at Meta help shape a future that will take us beyond what digital connection makes possible today—beyond the constraints of screens, the limits of distance, and even the rules of physics.

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Individual compensation is determined by skills, qualifications, experience, and location.

Compensation details listed in this posting reflect the base hourly rate, monthly rate, or annual salary only, and do not include bonus, equity or sales incentives, if applicable.

In addition to base compensation, Meta offers benefits.

Learn more about benefits at Meta.

Meta is proud to be an Equal Employment Opportunity employer.

We do not discriminate based upon race, religion, color, national origin, sex (including pregnancy, childbirth, reproductive health decisions, or related medical conditions), sexual orientation, gender identity, gender expression, age, status as a protected veteran, status as an individual with a disability, genetic information, political views or activity, or other applicable legally protected characteristics.

You may view our Equal Employment Opportunity notice here.

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You may view our Equal Employment Opportunity notice here.

Meta is committed to providing reasonable support (called accommodations) in our recruiting processes for candidates with disabilities, long term conditions, mental health conditions or sincerely held religious beliefs, or who are neurodivergent or require pregnancy-related support.

If you need assistance or an accommodation due to a disability, fill out the Accommodations request form Notice regarding automated employment decision tools in New York City Learn more about this here.

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3+ years of software engineering experience with a focus on AI, machine learning systems, or large-scale distributed systems that support model training