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.
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