This work involves building robust Machine Leanring (ML) modeling pipeline in Python to achieve desired ML model performance on metrics of customer interest (e.g. yield, tolerance). Broken into steps, the work is roughly an equal mix of tweaking core ML algorithms / pipelines for enhanced model performance with desired computational speed and/or creating deployment-ready software in our proprietary architecture.

This role offers a unique opportunity to deploy non-trivial ML modeling pipeline into production along with associated systems, and seeing the impact of those models in action at some of the world’s largest chip manufacturers for wide variety of use cases. This isnota Hadoop/Spark-focused big data role but rather an ML-focused role but may also include general software engineering tasks as well, depending on what is needed to make the overall product and platform successful and production-ready. Our deployment environment is markedly different from typical cloud deployment with constant access for updates or bug-fixes. Roughly stated, the production requirements are closer to a self-driving robot than a cloud-integrated image recognition service.

An ideal candidate has invested time in continuous learning and implementing algorithms either using an existing framework/library or from scratch and understands failure modes of various algorithms in production. Additionally, the ideal candidate has collaborated with Data Scientists to put ML code into production.

• Implementing production grade scalable ML algorithms and analytics solutions
• Improving computational performance of ML algorithms and analytics solutions
• Interact with Software Engineers and Data Scientists to create clean API interfaces

• MS or PhD in Computer Science, EE, Physics or related quantitative field
• 3+ years of experience in back-end development in python.
• 3+ years of engineering experience with significant exposure to algorithm implementation/machine learning libraries/frameworks.
• Experience with relational and NoSQL databases such as Postgresql/Cassandra/Redis or similar.
• Experience with API calls.
• Hands-on experience with containers such as Docker.
• Ability to analyze the bottlenecks and create high performance code.
• Good understanding of CPU and memory issues in parallel computing