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Salary
≈ $234k – $450k per year (Estimated)
Location
In office (Menlo Park)
Seniority
Staff · 8+ years exp
Employment
Full-Time

Confirmed on the employer's own hiring board on Oct 28, 2026. First seen by Alion on Oct 27, 2026. Meta scores B on the Alion truth index.

Overview
Company
Impact
Profile match
Meta Platforms is an American technology company founded in 2004 as Facebook and renamed in 2021 to signal its investment in immersive computing. It operates the largest family of social and messaging applications in the world, comprising Facebook, Instagram, WhatsApp, Messenger and Threads, and earns almost all of its revenue from advertising sold against that audience. Headquartered in Menlo Park, California, the company also develops the open Llama language models and the Reality Labs division behind Quest headsets and Ray-Ban smart glasses.
Meta is seeking experienced engineers to join AI Solutions and Automation + Support (ASA+S), where we apply state-of-the-art AI and autonomous agents to the products and support experiences used by billions of people. We build agents that resolve real problems for users, creators, and businesses, along with the platforms, evaluation systems, and model infrastructure that let those agents ship safely and at scale. This is applied AI where research meets production: you'll build and optimize LLM and agentic systems, design the data and evaluation foundations that make them trustworthy, and take frontier techniques from prototype to production. It's deliberately high-leverage work; what we prove once becomes a platform that other teams build on. If you want to work on genuinely novel agent problems and see your work reach real users quickly, this is the right team for you.

Responsibilities

Collaborate with cross-functional teams (product, design, operations, infrastructure) to build innovative application experiences Implement custom user interfaces using the latest programming techniques and technologies Analyze and optimize code for quality, efficiency, and performance, and provide feedback to peers during code reviews Set direction and goals for teams, lead major initiatives, provide technical guidance and mentorship to peers, and help onboard new team members Architect efficient and scalable systems that drive complex applications Identify and resolve performance and scalability issues, and drive large efforts to reduce technical debt Work on a variety of coding languages and technologies Establish ownership of components, features, or systems with an expert end-to-end understanding

Qualifications

Bachelor's degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience 8+ years of programming experience in a relevant language or 4+ years of experience with a PhD Bachelor's degree in Computer Science, Computer Engineering, a relevant technical field, or equivalent practical experience Programming experience in a relevant language Demonstrated experience driving change within an organization and leading complex technical projects Experience using data and analysis to explain technical problems and provide detailed feedback and solutions Master's degree or PhD in Computer Science or a related technical field Demonstrated grasp of data structures and algorithms, including graph theory and optimization techniques Understanding of information retrieval concepts, such as indexing, querying, and ranking Experience adhering to and implementing responsible, ethical AI practices (e.g., risk assessment, bias mitigation, quality and accuracy review) Experience with frameworks like TensorFlow, PyTorch, or Scikit-learn Knowledge of NLP techniques, including text preprocessing, tokenization, and sentiment analysis Experience with inference infrastructure, model evaluations, agent runtime, or synthetic data generation is a plus Demonstrated ability to integrate AI tools to optimize/redesign workflows and drive measurable impact (e.g., efficiency gains, quality improvements) Experience adhering to and implementing responsible, ethical AI practices (e.g., risk assessment, bias mitigation, quality and accuracy reviews) Demonstrated ongoing AI skill development (e.g., prompt/context engineering, agent orchestration) and staying current with emerging AI technologies

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