ML Engineer - Scalable Recommendation Systems

Meta Platforms, Inc.; Burlingame (IL); On-site; USD 177,000 - 251,000; Full time

Job summary

Meta Platforms, Inc. is seeking a Software Engineer, Machine Learning to lead a scalable ML team and own end-to-end ML life cycles.

You will work with top engineers and researchers to solve large-scale social data challenges and deliver impactful ML solutions across recommender, classification, and optimization problems. The role emphasizes building scalable ML models, adapting methods to distributed and GPU environments, and driving best practices while collaborating across teams.

Qualifications

• 6+ years of software engineering experience (4+ years if you have a PhD).
• 2+ years in ML, recommendation systems, pattern recognition, data mining, AI, or related field.
• Experience developing ML models at scale from inception to business impact.
• Knowledge developing and debugging in C/C++ and Java, or scripting in Python, Perl, PHP, and/or shell.
• Experience leading teams, owning projects, and setting technical direction.

Responsibilities

• Play a critical role in setting the direction and goals for a sizable team, in terms of project impact, ML system design, and ML excellence.
• Adapt standard ML methods to distributed clusters, multicore SMP, and GPU environments.
• Drive large efforts across teams to reduce technical debt, designing from first principles when appropriate.
• Lead a team to develop ML best practices and influence engineering culture.
• Be a go-to person to resolve complex production performance and evaluation issues.

Skills

Machine learning

C/C++

Java

Python

Leadership

Education

Bachelor's degree in Computer Science or related field

Masters/PhD in Computer Science or related field

Tools

Python

Perl

PHP

Shell scripting

Job description

Meta Platforms, Inc. is seeking a Software Engineer, Machine Learning to lead a scalable ML team and own end-to-end ML life cycles.

You will work with top engineers and researchers to solve large-scale social data challenges and deliver impactful ML solutions across recommender, classification, and optimization problems. The role emphasizes building scalable ML models, adapting methods to distributed and GPU environments, and driving best practices while collaborating across teams.