Widely considered to be one of the technology world’s most desirable employers, NVIDIA is an industry leader with groundbreaking developments in High-Performance Computing, Artificial Intelligence, and Visualization. The GPU, our invention, serves as the visual cortex of modern computers and is at the heart of our products and services. Today, we are increasingly known as “the AI computing company.” Join us at the forefront of technological advancement.
NVIDIA is seeking a Senior Applied Scientist / AI Engineer to join our core Sovereign AI engineering efforts. We are looking for a highly autonomous, deeply technical generalist who treats AI architecture as their craft, not just a day job. In this role, you will act as a full-stack AI problem solver, fluidly moving between model training, rigorous evaluation, and inference efficiency. You will design ablation studies, write high-performance code, and consistently upstream your learnings into NVIDIA's core libraries.
What you’ll be doing:
- End-to-End Model Training: Lead complex training experiments (Pre-training, CPT, SFT, and Alignment). You will design architecture ablations and upstream your optimized recipes into the Sovereign AI Playbook and NeMo core libraries.
- Rigorous Evaluation & Benchmarking: Deep dive into model evaluation strategies. You will design custom benchmarks, conduct eval-driven ablation experiments, and ensure models meet strict Sovereign AI quality and safety bars.
- Efficiency & Inference Optimization: Drive end-to-end modeling efficiency. You will operationalize advanced compression (Quantization, Distillation) and leverage inference engines (TensorRT-LLM, NIM) to ensure models are fast, cheap, and deployable.
- Relentless Execution: Act as a highly autonomous technical leader with a bias for action. You will take ambiguous problems across the entire LLM lifecycle and methodically drive them to shipped, reproducible solutions at the speed of light.
What we need to see:
- Masters or PhD in Computer Science, Machine Learning, or related field (or equivalent experience).
- 8+ years of technical experience, with 4+ years deeply focused on the LLM/Deep Learning lifecycle.
- Agility & Mastery: An insatiable ability to rapidly absorb new technologies, coupled with a deep, inside-out understanding of Transformers, scaling laws, and training dynamics.
- Programming Excellence: Expert-level Python and PyTorch, with a strong emphasis on writing scalable, production-grade code.
- High-Energy Builder: You possess a deep, personal investment in your work. We need highly organized, intrinsically motivated engineers who want to do their life's best work and bring extraordinary energy to the team.
- Framework Experience: Hands-on experience modifying large-scale frameworks like Megatron-LM, NeMo, TensorRT-LLM, vLLM, or Triton.
Ways to stand out from the crowd:
- Full-Stack ML Impact: A proven history of individually owning the training, evaluation, and deployment of a LLM/SLM.
- NVIDIA Stack Power User: Deep familiarity with the internal architecture of NeMo, Megatron, and the NIM ecosystem.
- Open Source Contributions: Meaningful PRs to major open-source LLM, evaluation, or inference repositories.
NVIDIA is committed to fostering a diverse 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.