Software Engineer, Machine Learning

Meta is seeking a Software Engineer with machine learning expertise to help build and scale ML systems that power products used by billions of people. In this role, you will design, develop, and optimize machine learning models and infrastructure across domains such as ranking, recommendation, natural language understanding, computer vision, or generative AI. You will work closely with product, data science, and infrastructure teams to translate research advances into reliable, high-impact production systems that shape how people connect, communicate, and discover content across Meta's family of apps.

Responsibilities

• Design, train, and deploy machine learning models for large-scale production systems spanning ranking, recommendation, classification, or generative AI applications
• Develop and maintain scalable ML pipelines covering data ingestion, feature engineering, model training, evaluation, and serving
• Collaborate with data scientists, product managers, and infrastructure engineers to translate ML research into reliable, production-ready systems
• Define and instrument metrics, dashboards, and alerting to monitor model health, detect regressions, and drive continuous improvement
• Lead technical design of ML components and systems, evaluating trade-offs across model quality, latency, throughput, and infrastructure cost
• Design and analyze experiments to validate model improvements, articulate assumptions, and make data-informed decisions that balance short-term gains with long-term product objectives
• Identify and resolve performance bottlenecks in model training and inference through profiling, optimization, and architectural improvements
• Proactively incorporate privacy, security, and responsible AI principles into ML system design and model development workflows
• Mentor other engineers on ML best practices, contribute to team coding standards, and drive adoption of tooling and automation that improves development efficiency
• Communicate technical decisions, trade-offs, and experimental outcomes clearly to cross-functional stakeholders across varying levels of technical background

Qualifications

• Bachelor's degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience
• 6+ years of software engineering experience with a focus on machine learning systems, model development, or ML infrastructure
• Experience developing and deploying machine learning models at scale, including feature engineering, model training, evaluation, and production serving
• Experience designing and analyzing A/B experiments to validate ML model improvements and drive data-informed product decisions
• Experience writing production-quality code in Python, C++, or a comparable language, with a track record of building maintainable, well-tested ML systems
• Experience owning end-to-end delivery of ML projects, including technical design, cross-functional collaboration, and operational readiness Experience with large-scale ML frameworks such as PyTorch or TensorFlow, and familiarity with distributed training and model serving infrastructure
• Experience applying responsible and ethical AI practices, including bias evaluation, risk assessment, and model quality reviews
• Experience in one or more specialized ML domains such as recommendation systems, natural language processing, computer vision, or generative AI
• Demonstrated ability to integrate AI-assisted development tools to improve engineering efficiency and code quality
• Demonstrated ability to integrate AI tools to optimize/redesign workflows and drive measurable impact (e.g., efficiency gains, quality improvements)
• Experience adhering to and implementing responsible, ethical AI practices (e.g., risk assessment, bias mitigation, quality and accuracy reviews)
• Demonstrated ongoing AI skill development (e.g., prompt/context engineering, agent orchestration) and staying current with emerging AI technologies