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Software Engineer, Machine Learning

Meta· Artificial Intelligence
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Menlo Park, CA$347k - $403kFull-timePosted 4d ago

About the role

Meta is seeking a distinguished Software Engineer with deep machine learning expertise to drive transformative advances across Meta's AI-powered products and platforms. You will operate at the intersection of foundational ML research and large-scale production systems, shaping the technical direction of machine learning infrastructure, modeling, and applied AI across the organization.

Responsibilities

  • Define and own the technical architecture of critical machine learning systems, including model training pipelines, inference infrastructure, and feature engineering platforms, ensuring reliability and scalability across billions of users
  • Identify and solve the most complex ML systems challenges across multiple product areas, including issues that span model quality, training efficiency, serving latency, and data integrity
  • Develop and establish extensible ML frameworks, modeling standards, and engineering practices that drive consistency and velocity across multiple engineering organizations
  • Lead cross-functional technical strategy for machine learning initiatives, aligning research, infrastructure, and product teams around multi-year roadmaps that balance short-term delivery with long-term architectural health
  • Apply AI-native workflows and tooling as a force multiplier to accelerate model development cycles, automate evaluation pipelines, and expand the scope of what engineering teams can deliver
  • Define new metrics and data-driven decision-making principles for long-term ML projects, connecting model performance signals to organization-level business outcomes
  • Proactively identify systemic reliability, privacy, and integrity risks in ML systems and build robust technical safeguards, partnering with compliance and policy teams to ensure responsible AI deployment
  • Mentor engineers across the organization on ML systems design, debugging complex model behavior, and building production-grade AI systems, establishing yourself as a sought-after technical coach and technical leader
  • Drive performance improvements across large-scale ML systems by identifying bottlenecks that span training, data loading, model serving, and hardware utilization, and leading cross-org efforts to resolve them
  • Influence the broader ML engineering community through technical publications, design frameworks, and cross-industry engagement that advances the field

Qualifications

  • Bachelor's degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience
  • 12+ years of experience designing, building, and deploying large-scale machine learning systems in production environments
  • Experience architecting end-to-end ML platforms spanning data pipelines, distributed training, model evaluation, and low-latency inference serving
  • Experience identifying and resolving complex, cross-system ML failures including issues in model quality, training stability, feature consistency, and serving correctness
  • Experience defining technical strategy and gaining organizational alignment across multiple engineering teams and cross-functional stakeholders
  • Experience communicating complex ML system designs and trade-offs in writing to both technical and non-technical audiences, including executive leadership

Skills

Required:Machine Learning systems designEnd-to-end ML platform architectureCross-functional leadershipTechnical communication to execs
Nice to have:ML tooling and automation
Last updated Oct 23, 2026. Interested? Apply on the company site.

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