Software Engineering Manager

Meta•Menlo Park, CA

About The Position

Meta Platforms, Inc. (Meta), formerly known as Facebook Inc., builds technologies that help people connect, find communities, and grow businesses. When Facebook launched in 2004, it changed the way people connect. Apps and services like Messenger, Instagram, and WhatsApp further empowered billions around the world. Now, Meta is moving beyond 2D screens toward immersive experiences like augmented and virtual reality to help build the next evolution in social technology. To apply, click “Apply to Job” online on this web page.

Requirements

Requires a Master’s degree in Computer Science, Computer Software, Machine Learning, Computer Engineering, Applied Sciences, Mathematics, Physics, or related field
Requires completion of a university-level course, research project, internship, or thesis in the following:
Coding in one of the following industry-standard languages: C, C++, Java, or C#
Python, PHP, or Haskell
Relational databases and SQL
Software development tools: Code editors (VIM or Emacs), and revision control systems (Subversion, GIT, or Perforce)
Linux, UNIX, or other nix-like OS as evidenced by file manipulation, advanced commands, and shell scripting
Core web technologies: HTML, CSS, or JavaScript
Build highly-scalable performant solutions
Data processing, programming languages, databases, networking, operating systems, computer graphics, or human-computer interaction
Applying algorithms and core computer science concepts to real world systems as evidenced by recognizing and matching patterns from different areas of computer science in production systems
Distributed systems
Machine learning libraries and frameworks such as TensorFlow, PyTorch, scikit-learn, or Keras
Statistical analysis, data modeling, or data visualization and
Big data technologies and distributed computing platforms such as Hadoop, Spark, or Cassandra

Responsibilities

Designing and Developing Intelligent Systems: They design and develop machine learning models, algorithms, and systems that can learn from data and make predictions or decisions.
Data Analysis and Modeling: System/Machine Learning Engineers analyze data to identify patterns, trends, and relationships, and use this knowledge to build predictive models and algorithms.
Model Training and Optimization: They train and optimize machine learning models using various techniques such as supervised, unsupervised, and reinforcement learning, and evaluate their performance using metrics such as accuracy, precision, and recall.
System Integration and Deployment: System/Machine Learning Engineers integrate machine learning models into larger systems and deploy them to production environments, ensuring they perform as expected and meet scalability and reliability requirements.
Performance Monitoring and Improvement: They monitor the performance of deployed machine learning models, identify areas for improvement, and implement changes to enhance their accuracy, efficiency, and scalability.
Collaboration with Cross-Functional Teams: System/Machine Learning Engineers work closely with product managers, data scientists, software engineers, and other stakeholders to understand business requirements, define project goals, and ensure successful project delivery.