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/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. What You'll Be Doing: Data Curation & 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. What We Need to See: 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. Ways to stand out from the crowd: Experience in some of the following areas is highly desirable: Distributed training at scale using frameworks like Megatron-LM and Megatron Bridge. It includes FSDP, TP/PP/CP/DP, heterogeneous or per-module parallelism, optimizer research, and efficient sparse or long-context attention. LLM/VLM Post-Training: Supervised fine-tuning (SFT), reinforcement learning for LLMs (e.g., PPO, GRPO, asynchronous RL), and large-scale RL frameworks such as NeMo-RL. Inference Efficiency: Model compression techniques including quantization (FP8, NVFP4, INT4), pruning, knowledge distillation, neural architecture search, and diffusion or non-autoregressive language models. Open-Source AI Infrastructure: Contributing to open-source AI frameworks such as Megatron-LM, Megatron Bridge, NeMo-RL, or Hugging Face Transformers along with experience in GPU performance optimization, distributed systems, latency/throughput analysis, and profiling of large-scale AI workloads. You will also be eligible for equity and benefits. Applications for this job 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 committed to fostering an inclusive work environment and proud to be an equal opportunity employer. As we highly value diversity in our current and future employees, we do not discriminate (including in our hiring and promotion practices) on the basis of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status or any other characteristic protected by law.