Senior AI Systems and Algorithms Engineer

Details

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

Responsibilities

• Data Curation and Readiness: Design scalable systems for preparing high-quality multimodal datasets for frontier foundation model training.
• Training Efficiency: Develop algorithms and systems that improve the scalability, efficiency, and cost of large-scale pre-training and post-training.
• Inference Efficiency: Advance techniques that improve inference performance, reduce deployment cost, and enable efficient serving across cloud and edge platforms.
• Open-Source AI Infrastructure: Develop reusable infrastructure and contribute brand-new model support to NVIDIA's open-source GenAI training platform.

Requirements

• MS or Ph.D. in Computer Science, AI, Applied Mathematics, or a related field, or equivalent experience.
• 5+ 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 using frameworks such as Megatron-LM and Megatron Bridge, including FSDP, tensor/pipeline/context/data parallelism, heterogeneous or per-module parallelism, optimizer research, and efficient sparse or long-context attention.
• LLM/VLM post-training, including supervised fine-tuning, reinforcement learning for LLMs such as PPO and 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, and diffusion or 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 expected base salary ranges for this position are $152,000–$241,500 USD for Level 3 and $184,000–$287,500 USD for Level 4. Base salary is determined based on location, experience, and other objective job-related factors. The role also includes eligibility for equity and benefits.

Applications will be accepted at least until November 6, 2026. This posting is for an existing vacancy. NVIDIA uses AI tools in its recruiting processes. NVIDIA is an equal opportunity employer committed to fostering an inclusive work environment.