Machine Learning Engineer (Technical Leadership)

Meta Careers; Singapore; On-site; SGD 300,000 - 420,000; Full time

Job summary

Meta is seeking principal engineers to join teams building cutting-edge ML products that scale to billions of users. You will design and deploy ML systems spanning model development, training infrastructure, and optimization, with a focus on safety, accuracy, and user experience across Meta's platforms.

We value deep technical leadership, multi-year roadmaps, and collaboration across functions. You will mentor teams, drive architectural decisions, and ship ML-powered features at global scale,

Qualifications

• Bachelor's degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience.
• Experience leading projects with industry-wide impact.
• Experience communicating and working across functions to drive solutions.
• Experience in mentoring/influencing technical direction across organizations through design reviews, proposals, or architectural decisions.
• Proven track record of planning multi-year roadmap in which short-term projects ladder to the long-term mission.

Skills

Project leadership

Cross-functional collaboration

Mentoring/tech direction

Roadmap planning

Cross-industry engineering

12+ years programming (Python/C++/Java

ML/artificial intelligence

ML models at scale (ads/e-commerce

Learning to Rank / GBM / RF / DL

Data structures & graphs

Shipping products at scale

Recommender systems / ranking

Responsible AI practices

PyTorch / TensorFlow

MLOps / monitoring / production ML

Large-scale training pipelines

ML-powered product shipping

Ongoing AI skill development

AI tooling integration for workflows

Publications or ML contributions

Education

Bachelor's degree in CS/CE or equivalent

Job description

Meta is seeking principal engineers to join our teams in building cutting-edge products that connect billions of people around the world. The ideal candidate will have deep industry experience building and deploying machine learning systems at scale, including model development, training infrastructure, and optimization. You will work on leveraging ML models to detect and enforce content to keep the platforms safe and create a better user experience across Meta's products — from payment fraud detection and click-through rate prediction to search ranking, content enforcement, and spam detection. This role involves applying advanced ML techniques to some of the most exciting and massive-scale prediction problems on the web.

• Bachelor's degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience
• Experience leading projects with industry-wide impact
• Experience communicating and working across functions to drive solutions
• Experience in mentoring/influencing technical direction across organizations through design reviews, proposals, or architectural decisions
• Proven track record of planning multi-year roadmap in which short-term projects ladder to the long-term mission
• Experience in driving large cross-functional/industry-wide engineering efforts
• 12+ years of experience in programming languages (Python, C++, or Java) with technical background
• 8+ years of experience in one or more of the following areas: machine learning, recommendation systems, pattern recognition, data mining or deep learning based methods
• Experience applying ML Models in a revenue (Ads or E-Commerce) or user facing product environment at scale (Matching or Demand Supply)
• Experience with relevant algorithms such as Learning to Rank, Gradient Boosting Machines, Random Forests, Deep Learning and similar algorithms
• Knowledge of data structures and algorithms, including graph theory and optimization techniques
• Experience in shipping products to millions of customers or have started a new line of product
• Expertise in Recommender Systems, Ranking, Relevance, Personalization or adjacent fields of ML
• Experience adhering to and implementing responsible, ethical AI practices (e.g., risk assessment, bias mitigation, quality and accuracy reviews)
• Experience with deep learning frameworks (PyTorch, TensorFlow) and ML infrastructure tools
• Familiarity with MLOps practices, model monitoring, and production ML systems
• Experience building and optimizing large-scale model training pipelines
• Experience shipping ML-powered products to millions of users or launching new ML product lines
• Demonstrated ongoing AI skill development (e.g., prompt/context engineering, agent orchestration) and staying current with emerging AI technologies
• Demonstrated ability to integrate AI tools to optimize/redesign workflows and drive measurable impact (e.g., efficiency gains, quality improvements)
• Publications or contributions to the ML research community