Senior AI Systems and Algorithms Engineer

at Nvidia

USD 152,000-287,500 per year

SENIOR

✅ On-site

Tech Stack
Tag name is followed by "@" symbol and proficiency level value. About proficiency levels:

1-2 — basic awareness. Minimal hands-on experience, and a rudimentary understanding of the technology's purpose;

3-6 — daily use. Comfortable and regular usage, capable of handling common tasks and challenges related to the technology;

7-9 — you are an expert, you can teach others, you know all the pitfalls and tricks;

10 — exceptional knowledge, comprehensive understanding, and adeptness in all aspects of the technology, including advanced problem-solving. Think twice before claiming or demanding such level.

AI @ 4 Algorithms Communication @ 6 Debugging @ 7 Deep Learning @ 7 Distributed Systems GPU GenAI LLM Machine Learning @ 7 Mathematics @ 4 Performance Optimization @ 7 Profiling PyTorch @ 6 Python @ 6 Reinforcement Learning

Details

NVIDIA is seeking a Senior GenAI Algorithms Engineer to advance the state of the art in foundation model development, training, and deployment. The role operates at the intersection of large-scale distributed training, reinforcement learning for LLMs and VLMs, model efficiency, multimodal AI, and open-source AI infrastructure. It spans the full GenAI lifecycle, including large-scale data preparation, training, post-training, inference optimization, and framework development.

Responsibilities

Design scalable systems for preparing high-quality multimodal datasets for frontier foundation model training.
Develop algorithms and systems that improve the scalability, efficiency, and cost of large-scale pre-training and post-training.
Advance techniques that improve inference performance, reduce deployment costs, and enable efficient serving across cloud and edge platforms.
Develop reusable infrastructure and contribute new model support to NVIDIA's open-source GenAI training platform.
Collaborate with research, product, and infrastructure teams to design new algorithms, optimize existing systems, and contribute to NVIDIA's open-source AI stack, including Megatron-LM, Megatron Bridge, and NeMo-RL.

Requirements

Master's degree or Ph.D. in Computer Science, AI, Applied Mathematics, or a related field, or equivalent experience.
5 or more years of relevant industry experience.
Strong foundation in machine learning, deep learning, and optimization.
Excellent software engineering skills, including Python and PyTorch.
Experience building high-performance software for large-scale AI systems.
Strong analytical, debugging, and performance optimization skills.
Excellent communication and collaboration skills.

Preferred Experience

Distributed training at scale, including Megatron-LM, Megatron Bridge, FSDP, TP/PP/CP/DP, heterogeneous or per-module parallelism, optimizer research, and efficient sparse or long-context attention.
LLM/VLM post-training, including supervised fine-tuning, PPO, GRPO, asynchronous reinforcement learning, and large-scale reinforcement learning frameworks such as NeMo-RL.
Inference efficiency and model compression techniques, including FP8, NVFP4, and INT4 quantization, pruning, knowledge distillation, neural architecture search, diffusion models, and non-autoregressive language models.
Contributions to open-source AI frameworks such as Megatron-LM, Megatron Bridge, NeMo-RL, or Hugging Face Transformers.
GPU performance optimization, distributed systems, latency and throughput analysis, and profiling of large-scale AI workloads.

Compensation And Benefits

The base salary range is USD 152,000–241,500 for Level 3 and USD 184,000–287,500 for Level 4. Base salary is determined based on location, experience, and the pay of employees in similar positions. The role also includes eligibility for equity and benefits.

Applications will be accepted at least until August 9, 2026. NVIDIA is an equal opportunity employer committed to an inclusive work environment.