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Sr CUDA Software Engineer

Remote:

Full Remote

Contract:

Full time

Experience:

Senior (5-10 years)

Work from:

Bangladesh

Offer summary

Qualifications:

Bachelor's degree in Computer Science or related fields, 5+ years of software development experience, 2+ years of CUDA development experience, Proficiency in C/C++, Experience with CUDA version 12.0 or above.

Key responsabilities:

Identify and resolve performance issues with CUDA
Analyze and improve algorithm performance
Stay updated with new CUDA technologies and practices

Yo Hr Consultancy SME https://www.yohrconsultancy.com/

11 - 50 Employees

See more Yo Hr Consultancy offers

Job description

Your missions

CUDA Developer/Engineer

Experience: 5 - 20 Years

Location- Permanent Remote Anywhere in the world

Opportunity: Full Time, Contract, 8 hours (4 Hrs overlap with PST timezone)

Contract length: 12-Months

Must-Have:

Total Years Of exp- 5+ years experience mandatory

Mandatory Skills: C++ - 5 years, LLM - 2 years, 2 years exp in CUDA development.

Required Qualifications:

Bachelor’s degree in Computer Science, Computer Engineering, or related fields.
Minimum of 5 years of software development experience, with at least 2 years specifically in CUDA development.
Ability to solve complex problems and work both independently and in a team.
Good communication skills and ability to work in a collaborative environment.

Knowledge and Skills:

Proficiency in C/C++.
Experience with code optimization for performance and efficient resource usage.
Experience with CUDA version 12.0 or above (12.3 preferred).
Knowledge of frameworks and libraries like cuBLAS and cuDNN.
CUDA Performance Optimization.

Job Description:

Position Overview: The ideal candidate should have a strong background in parallel computing and performance optimization using CUDA.

Responsibilities:

Identify and resolve performance issues and bugs related to CUDA usage.
Analyze and improve algorithm performance through efficient parallelization.
Stay updated with new technologies and best practices in CUDA and parallel computing.