Machine Learning Engineer (Technical Leadership)

Role Overview

Meta is looking for principal engineers to join their teams that build innovative products connecting billions globally. The focus is on deploying scalable machine learning systems covering model development, training infrastructure, and model optimization. The engineer will leverage ML to detect content and enforce platform safety while enhancing user experience across Meta's suite of products such as payment fraud detection, click-through prediction, search ranking, content enforcement, and spam detection. This position involves applying advanced ML solutions to large-scale web prediction challenges.

Key Responsibilities

• Lead the ML strategy and set the technical vision to enhance machine learning capabilities organization-wide.
• Develop full machine learning pipelines from data processing to model training, validation, and deployment.
• Manage experimentation and A/B testing frameworks to assess and improve model effectiveness.
• Create scalable classifiers and ML tools using methods such as deep learning, regression, and rules-based models.
• Optimize ML techniques for parallel computing environments including distributed clusters, multicore SMP, and GPUs.
• Collaborate with research teams to convert state-of-the-art ML research into practical production implementations.
• Guide and mentor ML engineers across various teams to elevate machine learning standards.
• Discover new ML application areas within the company and influence prioritization and resource allocation.
• Communicate complex machine learning systems and architectural choices clearly to both technical and non-technical audiences.

Minimum Qualifications

• Bachelor’s degree in Computer Science, Computer Engineering, or a related technical discipline, or equivalent practical experience.
• Proven leadership experience on projects with significant industry impact.
• Effective cross-functional communication and collaboration skills.
• Experience mentoring and steering technical decisions through design reviews and architectural proposals.
• Track record of planning multi-year project roadmaps aligning short-term actions to long-term goals.
• Proven leadership in extensive cross-functional engineering projects.
• 12+ years experience with programming languages such as Python, C++, or Java.
• 8+ years expertise in areas including machine learning, recommendation systems, pattern recognition, data mining, or deep learning.

Preferred Qualifications

• Applied ML model experience in revenue-driven or user-facing products at a large scale (e.g., Ads, E-Commerce).
• Familiarity with algorithms like Learning to Rank, Gradient Boosting Machines, Random Forests, and Deep Learning.
• Strong understanding of data structures, algorithms, graph theory, and optimization methods.
• Experience launching products reaching millions of users or creating new product lines.
• Specialization in Recommender Systems, Ranking, Personalization or related ML fields.
• Commitment to responsible AI practices including risk assessment, bias mitigation, and quality assurance.
• Proficiency with deep learning frameworks such as PyTorch and TensorFlow, plus ML infrastructure tools.
• Knowledge of MLOps, model monitoring, and production ML system best practices.
• Proven ability to build and scale large ML training pipelines.
• Continuous skill enhancement in AI, including prompt engineering and agent orchestration.
• Demonstrated integration of AI tools to optimize workflows and enhance quality and efficiency.
• Contributions to the ML research community via publications or open source involvement.

About Meta

Meta develops technologies enabling global social connections, communities, and business growth. Since launching Facebook in 2004 and expanding with Messenger, Instagram, and WhatsApp, Meta aims to pioneer immersive experiences beyond traditional screens through augmented and virtual reality. Careers at Meta shape the future of digital connections transcending physical and technical boundaries.

Additional Information

Compensation depends on skills, experience, qualifications, and location. The stated pay reflects base salary components and excludes performance bonuses, equity, or incentives. Additionally, Meta offers comprehensive benefits for its employees.