PhD ML Systems Engineer Intern

Meta; Redmond; On-site; AUD 116,000 - 201,000; Full time

Job summary

Meta is seeking a Software Engineer Intern, Machine Learning (PhD) to build scalable ML systems and models behind Meta’s products. You will develop classifiers, work across teams, and adapt ML methods to distributed, multicore, and GPU environments.

The 12-week internship starts in 2026 and matches you to a team based on interests and experience. Responsibilities include delivering code with engineering teams, crafting feature roadmaps, and applying ML to large-scale datasets.

Qualifications

• PhD in Computer Science, Computer Vision, Machine Learning, or related field.
• Research/work in ML, DL, RL, NLP, recommender systems, pattern recognition, data mining, AI, or CV.
• Experience in systems software or algorithms.
• Experience coding in Java, C/C++, Perl, PHP, or Python.
• Interpersonal experience: cross-group collaboration.
• Must have work authorization in country of employment.

Responsibilities

• Develop highly scalable classifiers and tools leveraging machine learning, regression, and rules-based models.
• Suggest, collect and synthesize requirements and create effective feature roadmap.
• Code deliverables in tandem with the engineering team.
• Adapt standard machine learning methods to best exploit modern parallel environments (distributed clusters, multicore SMP, GPUs).
• Perform specific responsibilities which vary by team.

Skills

Programming languages

Machine learning

Distributed systems

Cross-group collaboration

Education

PhD in CS/ML/related

Tools

Hadoop/HBase/Pig

MapReduce/Sawzall/Bigtable

Job description

Meta is seeking a Software Engineer Intern, Machine Learning (PhD) to build scalable ML systems and models behind Meta’s products. You will develop classifiers, work across teams, and adapt ML methods to distributed, multicore, and GPU environments.

The 12-week internship starts in 2026 and matches you to a team based on interests and experience. Responsibilities include delivering code with engineering teams, crafting feature roadmaps, and applying ML to large-scale datasets.