NVIDIA is leading company of AI computing. At NVIDIA, our employees are passionate about AI, HPC , VISUAL, GAMING. SA team is more focusing to bring NVIDIA new technology into difference industries. This role focuses on NVIDIA Inference Microservices (NIM), inference / RL rolloutperformance, and AI workflow enablement for LLM, VLM, and other generative AI workloads. It is a highly hands-on position at the intersection of model optimization, inference infrastructure, and customer solution delivery.
What you’ll be doing:
- Drive the implementation, deployment, and optimization of NVIDIA Inference Microservices (NIM) solutions for enterprise and industry AI workloads.
- Package and serve open-source, NVIDIA, and customer-proprietary models through NIM with standardized, containerized APIs for on-premises, cloud, and hybrid environments.
- Optimize high-volume inference and rollout workloads for LLMs and VLMs.
- Evaluate and tune the NIM models.
- Deliver technical projects, demos and client support tasks as directed by the Solution Architecture Leadership.
- Provide technical support and guidance to customers, facilitating the adoption and implementation of NVIDIA technologies and products.
- Collaborate with cross-functional teams to enhance and expand our AI solutions portfolio.
What we need to see:
- Master’s degree or higher in Computer Science, Machine Learning, Electrical Engineering, Mathematics, or a related technical field, or equivalent experience.
- 2+ years of hands-on experience in machine learning engineering, applied research, LLM/VLM inference, or RL rollout.
- Production-quality Python and PyTorch skills, including distributed GPU training, solution, profiling, debugging, memory optimization.
- Working knowledge of transformer architectures, performance optimization, rollout sampling strategies, structured generation, and model-quality evaluation.
- Strong written and verbal communication skills, with the ability to collaborate effectively across research, engineering, infrastructure, product, and customer-facing teams.
Ways to stand out from the crowd:
- Publications, open-source contributions, or significant technical projects, LLM/VLM, agent systems.
- Experience applying programmatic verification, simulators, compilers, execution sandboxes, APIs, or external tools as reward sources for model training. agent system.
- Familiar with oss RL framework such as SLIME, Nemo-RL.
- Familiarity with enterprise AI deployment, customer adaptation, or adapting foundation models to specialized vertical domains.
Skills Required
- Master’s degree or higher in Computer Science, Machine Learning, Electrical Engineering, Mathematics, or a related technical field, or equivalent experience
- 2+ years of hands-on experience in machine learning engineering, applied research, LLM/VLM inference, or reinforcement-learning rollout
- Production-quality Python and PyTorch skills
- Experience with distributed GPU training, solution profiling, debugging, and memory optimization
- Working knowledge of transformer architectures, performance optimization, rollout sampling strategies, structured generation, and model-quality evaluation
- Strong written and verbal communication skills and ability to collaborate across research, engineering, infrastructure, product, and customer-facing teams
- Publications, open-source contributions, or significant technical projects involving LLMs, VLMs, or agent systems
- Experience with programmatic verification, simulators, compilers, execution sandboxes, APIs, or external tools as reward sources for model training
- Familiarity with open-source reinforcement-learning frameworks such as SLIME or NeMo-RL
- Familiarity with enterprise AI deployment, customer adaptation, or adapting foundation models to specialized vertical domains
NVIDIA Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about NVIDIA and has not been reviewed or approved by NVIDIA.
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Equity Value & Accessibility — Equity awards and a discounted ESPP are highlighted as core parts of total compensation, enabling employees to share in the company’s success. Stock-based compensation and the two-year lookback ESPP are consistently described as especially valuable.
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Healthcare Strength — Health coverage is portrayed as robust, with comprehensive medical, dental, and vision options alongside mental health support and on-site care resources. Employer HSA contributions and wellness perks reinforce the depth of the offering.
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Retirement Support — Retirement programs are depicted as strong, featuring a meaningful 401(k) match with Roth options and support for Mega Backdoor Roth contributions. These elements position long-term savings as a notable advantage of the total rewards package.
NVIDIA Insights
What We Do
NVIDIA’s invention of the GPU in 1999 sparked the growth of the PC gaming market, redefined modern computer graphics, and revolutionized parallel computing. More recently, GPU deep learning ignited modern AI — the next era of computing — with the GPU acting as the brain of computers, robots, and self-driving cars that can perceive and understand the world. Today, NVIDIA is increasingly known as “the AI computing company.”
