This opportunity is part of Career Day 2026. Apply now for a chance to be shortlisted and interview directly with employers at the event!

To apply for this job, you need to complete both steps below:

STEP 1:

Please send your CV to this email to submit your application directly to the company: 

careerservice@vinuni.edu.vn

Your application will only be received by the Recruiter if submitted via the above mail.

STEP 2:

Kindly scroll to the bottom of this page and complete the short VinUni Tracking Form.

Filling out this form alone does not count as applying. Kindly remind that this form is not part of the company’s application process. It only helps the Careers, Alumni, Industry, and Development (CAID) Department discover more opportunities and follow up in case of system issues.

We’re hiring a Machine Learning System Engineer to drive the design, prototyping, and 
optimization of deep learning systems that power our in-house product and AI solutions for 
datacenters. This role blends strong ML/DL fundamentals with the ability to move fast —  taking 
ideas from concept, to measurable experiments, to production-ready systems. You’ll work at the 
bleeding edge of deep learning, where software meets hardware, contributing to 
performance-critical infrastructure and exploring new ways to push large-scale training and 
inference forward. 
If you're excited by fast iteration, deep systems insight, and applying ML knowledge to real 
engineering problems, this role is for you. 
Responsibilities: 
● Develop and maintain software systems in Python and C++ that support deep learning 
training and inference workflows. 
● Bridge the gap between ML research and systems engineering by turning ideas into fast, 
reliable infrastructure. 
● Analyze and optimize bottlenecks in model execution across the stack 
● Rapidly prototype and validate new approaches to model parallelism, memory 
management, and scheduling. 
● Benchmark, validate, and improve the performance and reliability of deep learning 
frameworks and runtime systems. 
● Work closely with infrastructure, hardware, and research teams to co-design solutions 
across the ML stack. 
● Write clean, testable, and well-documented code to ensure long-term maintainability. 
● Stay informed about emerging trends in deep learning systems and help evaluate new 
tools and techniques. 
Requirement skills:  
● 2+ years of experience in a similar position 
● Solid understanding of machine learning and deep learning fundamentals (e.g., 
backpropagation, optimization algorithms, model architectures). 
● Proficiency in Python, with working knowledge of C++ or willingness to work at the 
system level. 
● Hands-on experience with deep learning frameworks (e.g., PyTorch, Tensorflow, Hugging 
Face Transformers). 
● Experience designing and running experiments — from idea to implementation, analysis, 
and iteration. 
● Comfortable navigating Linux-based environments and using common developer tools 
(e.g., Git, Docker, profilers). 
● Strong English communication skills and the ability to explain complex technical results 
clearly. 
● Curious, execution-focused mindset — able to prioritize and iterate quickly in a 
performance-oriented team. 
Preferred skills: 
● Familiarity with the inner workings of machine learning frameworks like Pytorch, 
Tensorflow or equivalent 
● Experience with large-scale or distributed training. 
● Experience with performance characteristics of decoder-only foundational large 
language models (GPT, Llama, Qwen, Deepseek, etc.) 
● Exposure to low-level performance tuning & optimizations (CUDA, XLA, custom kernels). 
● Familiarity with ML system topics like model parallelism, compiler stacks (e.g., TVM, 
TorchDynamo & TorchInductor), quantization, or memory management. 
● Prior work in applied ML systems research or open-source contributions. 
What we offer: 
● The opportunity to work on cutting-edge problems at the intersection of deep learning, 
systems engineering, and hardware acceleration. 
● Access to state-of-the-art compute resources and infrastructures to support 
experimentation and development. 
● Clear growth opportunities and support for continuous learning through conferences, 
papers, and internal knowledge-sharing. 
Benefits: 
● Training opportunity: With Korean experts 
● Salary Package: Competitive & 100% salary in probation time 
● Annual Leaves: 12 days of paid annual leave  
● Additional company benefits, including but not limited to: 
○ 13th month salary 
○ Annual Health Check-Up 
○ Equipment Upgrades 
○ Sports Club Sponsorship 
○ Monthly Happy Dinners