Machine Learning Engineer

Meta Platforms, Inc.; Bellevue (WA); On-site; USD 110,000 - 140,000; Full time

Job summary

Meta Platforms, Inc. is seeking a Machine Learning Engineer in Bellevue, Washington. The role involves researching, designing, and developing software for complex predictive problems, utilizing deep learning frameworks.

The ideal candidate will have a Master's degree and 3 years of experience in machine learning frameworks, recommendation systems, and data processing. Collaboration with cross-functional teams and ensuring best practices in code are essential aspects of this role.

Qualifications

• Master's degree in Management Information Systems, Computer Science, or related field.
• 3 years of experience in machine learning, recommendation systems, and data processing.
• Experience with deep learning frameworks like PyTorch, MXNet, or TensorFlow.

Responsibilities

• Research, design, and develop software for social data and prediction problems.
• Collaborate with cross-functional teams to integrate machine learning models.
• Participate in code reviews and ensure solutions meet industry standards.

Skills

Machine Learning Frameworks

Recommendation Systems

Data Processing

Python

Linux

SQL

Education

Master’s degree in relevant field

Tools

Hadoop

TensorFlow

Git

Job description

Machine Learning Engineer Responsibilities

• Research, design, develop, and test operating systems-level software, compilers, and network distribution software for massive social data and prediction problems.
• Have industry experience working on a range of ranking, classification, recommendation, and optimization problems, such as payment fraud, click-through or conversion rate prediction, click-fraud detection, ads/feed/search ranking, text/sentiment classification, collaborative filtering/recommendation, or spam detection.
• Working on problems of moderate scope, develop highly scalable systems, algorithms and tools leveraging deep learning, data regression, and rules-based models.
• Suggest, collect, analyze and synthesize requirements and bottleneck in technology, systems, and tools.
• Develop solutions that iterate orders of magnitude with a higher efficiency, efficiently leverage orders of magnitude and more data, and explore state-of-the art deep learning techniques.
• Receiving general instruction from supervisor, code deliverables in tandem with the engineering team.
• Collaborate with DevOps team to develop and deploy machine learning models into production environments and ensure smooth operation of machine learning models in production.
• Work with data engineers to design and implement data pipelines for large-scale machine learning tasks.
• Collaborate with cross-functional teams to integrate machine learning models into larger systems.
• Participate in code reviews and ensure that all solutions are aligned with industry standards and best practices.
• Work with data scientists to analyze and interpret model performance metrics.

Minimum Qualifications

• Requires a Master’s degree in Management Information Systems, Computer Science, Computer Software, Computer Engineering, Applied Sciences, Mathematics, Physics, or related field and three years of work experience in the job offered or in a computer-related occupation. Requires three years of experience in the following:
• Machine Learning Framework(s): PyTorch, MXNet, or Tensorflow
• Machine learning, recommendation systems, computer vision, natural language processing, data mining, or distributed systems
• Translating insights into business recommendations
• Hadoop, HBase, Pig, MapReduce, Sawzall, Bigtable, or Spark
• Scripting languages: Perl, Python, PHP, or shell scripts
• 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
• 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.