Base Salary
$347k - $403k/yr
Responsibilities
- Own the technical architecture of large-scale ML systems, including model training pipelines, inference infrastructure, and feature engineering platforms.
- Identify and resolve complex ML systems issues involving model quality, training efficiency and stability, serving latency and correctness, data integrity, and feature consistency.
- Develop extensible ML frameworks, modeling standards, and engineering practices across multiple organizations.
- Lead cross-functional technical strategy and multi-year roadmaps spanning research, infrastructure, and product teams.
- Build AI-native workflows and tooling to accelerate model development, automate evaluation, and improve engineering productivity.
- Define metrics and data-driven decision-making principles connecting model performance to business outcomes.
- Identify reliability, privacy, and integrity risks and implement technical safeguards for responsible AI deployment.
- Mentor engineers on ML systems design, complex model debugging, and production-grade AI systems.
- Lead performance improvements across training, data loading, model serving, and hardware utilization.
- Influence the ML engineering community through technical publications, design frameworks, and industry engagement.
Requirements
- Bachelor's degree in Computer Science, Computer Engineering, a relevant technical field, or equivalent practical experience.
- 12+ years of experience designing, building, and deploying large-scale machine learning systems in production.
- Experience architecting end-to-end ML platforms covering data pipelines, distributed training, model evaluation, and low-latency inference serving.
- Experience resolving complex cross-system ML failures involving model quality, training stability, feature consistency, and serving correctness.
- Experience defining technical strategy and gaining alignment across multiple engineering teams and stakeholders.
- Experience communicating complex ML system designs and trade-offs to technical and non-technical audiences, including executives.
- Preferred: experience building AI-native developer tooling or automation that accelerates ML experimentation and deployment.
- Preferred: experience applying ML across ranking and recommendation, generative AI, computer vision, or natural language understanding.
- Preferred: industry-recognized contributions such as publications, open-source frameworks, or widely adopted architectural patterns.
- Preferred: experience implementing responsible and ethical AI practices, including risk assessment, bias mitigation, and quality reviews.
- Preferred: ongoing AI skill development in areas such as prompt/context engineering and agent orchestration.
- Preferred: experience with large-scale foundation model training, fine-tuning, or inference optimization across distributed hardware clusters.
Benefits
- Bonus, equity, and benefits are offered in addition to base salary.
Categories
About Meta
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