Senior Full Stack Engineer, Deep Learning Algorithms

Nvidia

Multiple Locations

You will be responsible for implementing and maintaining tools, and infrastructure that enable our teams to productize NVIDIA SW stack: DL Frameworks, DL models, AI services and other tools and technologies. Are you ready for this challenge?

What you’ll be doing:

Automating and optimizing testing of Deep Learning models and AI Services from different data domains with focus on inference;
Developing shared utilities for setting up systems, running tests, recording results and visualization on dashboards;
Configuring, maintaining, and building solutions on industry-standard tools (e.g. GitLab, Docker, ElasticSearch etc.);
Lead best-practices for building, testing, and releasing software including AI Services and DL models;
Identifying infrastructure needs and translating them into action;
Building tools for automatic content generation mechanisms that saves dozens of engineering hours.

What we need to see:

BSc or MS degree in Computer Science, Computer Architecture or related technical field;
5+ years of work experience in software development;
Excellent Python programming skills, and a deep understanding of OOP concepts;
Experience in building both back-end services (e.g. Flask, FastAPI, Django) services and front-end (e.g. JS, React, Vue, Dash, Streamlit);
Understanding of Deep Learning on the level that allows benchmarking DL models;
Familiarity with DevOps concepts such as CI/CD, Docker, Jenkins, and automation tools;
Strong time-management and organization skills for coordinating multiple initiatives and priorities;
Ability to utilize new technologies and products in very complex projects;
Good communication and documentation habits.

Ways to stand out from the crowd:

Deep understanding of containerization technologies such as Docker;
Experience in building monitoring or dashboarding solutions;
Hands-on in configuring complex CI pipelines;
Experience with HPC based compute clusters and scheduling solutions like Slurm or familiarity with orchestration tools such as Kubernetes;
Hands-on experience working in cloud environment.

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