Software Engineer – Machine Learning

Software Engineer – Machine Learning

Requirements

• Requires a Master’s degree in Computer Science, Computer Software, Computer Engineering, Computational Data Science, Applied Sciences, Mathematics, Physics, or related field.
• Completion of a university-level course, research project, internship, or thesis in the following:
• 1. Deep learning systems
• 2. Methods, mathematics and algorithms needed to do research and applications in machine learning
• 3. Machine learning, recommendation systems, computer vision, natural language processing, data mining, or distributed systems
• 4. Analytic algorithms (e.g. classification, clustering, ranking, prediction)
• 5. Developing and debugging in C, C++, Java, or Python
• 6. Interactive analysis (Jupyter and R) and visualization techniques for data analysis
• 7. Distributed file systems, NoSQL databases, and object storage systems including: HDFS, CephFS, HBASE, MongoDB, Cassandra, DynamoDB, S3, and Swift
• 8. Software development tools: Code editors (VIM or Emacs), and revision control systems (Subversion, GIT, or Perforce)
• 9. Linux, UNIX, or other *nix-like OS as evidenced by file manipulation, advanced commands, and shell scripting
• 10. Solution design (including architecture, framework APIs, cloud computing), and solution optimization and performance measurement on real-world tasks
• 11. Data processing, programming languages, databases, networking, operating systems, computer graphics, or human-computer interaction
• 12. 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
• 13. Designing, implementing, and debugging real distributed systems
• 14. Compilers, networks, operating systems, and computer architecture.

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, e.g. 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.
• Adapt standard machine learning methods to best exploit modern parallel environments (e.g. distributed clusters, multicore SMP, and GPU).
• Leverage build and compile tooling.
• Develop unit and regression tests.
• Submit candidate code changes to internal code review system.
• Regular review code submissions of other engineers.
• Communicate progress broadly via internal messaging systems.

Preferred Qualifications

No preferred qualifications provided.