Machine Learning Engineer (Technical Leadership)

JobsCloseBy Editorial Insights

Meta is seeking a Machine Learning Engineer to join our integrity engineering team in New York City for a full-time onsite role. The ideal candidate will have deep hands-on experience building and deploying ML systems at scale, from data pipelines and model training to evaluation, deployment and optimization, and a proven ability to lead cross-functional initiatives that deliver measurable impact. Expect 12+ years of programming experience in Python, C++, or Java, 8+ years in ML related fields, and a strong background with PyTorch or TensorFlow, MLOps, and production monitoring. To apply, highlight end to end projects, leadership across teams, ethical AI practices, and concrete results with metrics, plus clear stakeholder communication.

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Meta is seeking a Machine Learning Engineer to join our integrity engineering team. 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.

Responsibilities
Drive the team's ML strategy & technical direction to pursue opportunities that advance machine learning capabilities across the organization
* Design and develop end-to-end machine learning systems, from data pipelines to model training, evaluation, and deployment
* Lead experimentation and A/B testing frameworks to measure and optimize model performance
* Build highly scalable classifiers and ML tools leveraging deep learning, data regression, and rules-based models
* Adapt and optimize machine learning methods for modern parallel environments (e.g., distributed clusters, multicore SMP, and GPU)
* Partner with research teams to translate cutting-edge ML research into production systems
* Mentor and influence ML engineers across organizations, raising the bar for ML best practices
* Identify new ML opportunities for the larger organization and influence staffing/prioritization of these initiatives
* Effectively communicate complex ML systems and architectural decisions to technical and non-technical stakeholders

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 engineers across organizations
* Proven track record of planning multi-year roadmap in which short-term projects ladder to the long-term vision
* 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 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