Software Engineer, Machine Learning

Meta•Menlo Park, CA

2d•$154,003 - $217,000

About The Position

Meta is seeking a Software Engineer with machine learning expertise to build and scale ML systems that power products used by billions of people. The role involves designing, developing, and optimizing machine learning models and infrastructure in areas such as ranking, recommendation, natural language understanding, computer vision, and generative AI. The engineer will work with product, data science, and infrastructure teams to translate research advances into reliable, high-impact production systems across Meta's family of apps.

Requirements

Bachelor's degree in Computer Science, Computer Engineering, a relevant technical field, or equivalent practical experience.
At least 6 years of software engineering experience focused 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 inform product decisions.
Experience writing production-quality code in Python, C++, or a comparable language, and building maintainable, well-tested ML systems.
Experience owning end-to-end delivery of ML projects, including technical design, cross-functional collaboration, and operational readiness.

Nice To Haves

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 specialized ML domains such as recommendation systems, natural language processing, computer vision, or generative AI.
Ability to integrate AI-assisted development tools to improve engineering efficiency and code quality.
Ability to integrate AI tools to optimize or redesign workflows and drive measurable impact, such as efficiency gains or quality improvements.
Experience implementing responsible and ethical AI practices, including risk assessment, bias mitigation, and quality and accuracy reviews.
Ongoing AI skill development, such as prompt or context engineering and agent orchestration, and staying current with emerging AI technologies.

Responsibilities

Design, train, and deploy machine learning models for large-scale production systems, including ranking, recommendation, classification, and generative AI applications.
Develop and maintain scalable ML pipelines for data ingestion, feature engineering, model training, evaluation, and serving.
Collaborate with data scientists, product managers, and infrastructure engineers to turn ML research into reliable production systems.
Define and instrument metrics, dashboards, and alerting to monitor model health, detect regressions, and support continuous improvement.
Lead technical design of ML components and systems, assessing trade-offs involving 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 model training and inference performance bottlenecks through profiling, optimization, and architectural improvements.
Incorporate privacy, security, and responsible AI principles into ML system design and model development workflows.
Mentor engineers on ML best practices, contribute to coding standards, and promote tooling and automation that improves development efficiency.
Communicate technical decisions, trade-offs, and experimental outcomes clearly to cross-functional stakeholders with varying technical backgrounds.