Nvidia Senior Applied Researcher Machine Learning 

Israel, Center District, Raanana 

670755023

What you'll be doing:

• Explore high-level, undefined ideas and solve real-life problems using structured and unstructured data.

• Craft proof-of-concept rooted in first principles that apply modern data science techniques to operation use cases.

• Collaborate in a multi-disciplinary environment with domain experts in various fields such as networking, high performance computing for AI, telemetry etc.

• Develop a strategic vision for Nvidia networking together with adjacent architects and research groups.

• Define the data pipelines and ML architecture for SaaS for handling hyper scale data problems.

• Support software developers to migrate prototyped to end-to-end pipelines that are suitable for deployment in production environments.

What we need to see:

• M.Sc. or PhD. in Science or Engineering

• 12+ years of relevant experience

• Validated excellent and industry experience in data science or machine learning with a variety of ML/DL algorithms and their application

• Consistent record of staying ahead of technology envelope, understand pioneering research, dabble into new technologies to develop practical applications and generate innovative ideas.

• Great motivation, with strong interpersonal skills and the ability to communicate highly technical concepts with non-technical audiences

• "Can do attitude" - ability to succeed in ambiguous settings where part of the challenge is to define it.

• Strong programming skills in Python (including unit-tests, CI&CD etc), as well as comfort using Linux and typical development tools (e.g., GitHub, Docker)

• Experience in large scale data systems (on-prem and/or cloud).

• Proficiency in deep learning frameworks.

Ways to stand out from the crowd:

• Past senior technical roles such as principle data scientist, team leader, tech lead, head of ML in a startup. Publications in peer-reviewed journals or conferences. Previous real-world experiencing developing models for anomaly detection, predictive forecasting, root-cause-analysis use cases.

• Experience in developing and deploying ML pipelines at large scales (TB+). Beyond supervised learning: optimization using Reinforcement learning and adaptive experimentation. Experience with ML deployment lifecycle including model monitoring and retraining.

• Experienced with networking, cloud, data-center, edge computing technologies.