NVIDIA has been redefining computer graphics, PC gaming, and accelerated computing for more than 30 years. Today, we’re tapping the unlimited potential of AI to define the next era of computing. As part of the digital biology team, you’ll build and scale the AI software that powers breakthroughs in drug discovery and biological sciences. If you love moving between science, code, and the infrastructure that makes it reliable, fast, and cost‑efficient, you’ll feel at home here.What you will be doing:<ul><li>Own testing-at-scale and reliability pipelines: Build hermetic, reproducible test matrices across GPUs SKUs, multi-node scale, and scientific parameters relevant to the biology space. Create integration and performance test harnesses for large models.</li><li>Productize AI algorithms: Ship LLMs and geometric deep learning models into production-quality services and SDKs; ensure observability, reproducibility, and model/package versioning.</li><li>Develop and deploy distributed learning systems and tools to synchronize and debug workloads on thousands of GPUs.</li><li>Collaborate across teams: Partner with applied research, AI infrastructure, and full‑stack teams; contribute to and upstream improvements across the open‑source ecosystem.</li><li>Be hands‑on: Dive into whatever is needed—infra, glue code, tests, or docs—to unblock the team and ship.</li></ul>What we need to see:<ul><li>3+ years of relevant experience.</li><li>BS/MS in CS, EE, Math, Physics, or equivalent experience.</li><li>Python, pytorch expertise</li><li>CI/CD and automation experience: GitHub Actions, YAML workflows, runners, authentication, caching, artifact stores, and release pipelines.</li><li>Distributed training fundamentals: DDP/FSDP, NCCL, mixed precision, data/pipe/tensor parallelism.</li><li>MLOps for AI: Linux, bash, containers (Docker/NGC), SLURM and/or Kubernetes.</li><li>Recognized for ownership and technical leadership, with excellent communication and a bias for action.</li></ul>Ways to stand out from the crowd:<ul><li>Worked in mixed applied science and engineering teams, familiar with the production-grade vs agility balance required to make forward progress.</li><li>Systems intuition for compute efficiency: kernel optimization, IO/data pipelines, and performance tradeoffs.</li><li>A natural interest in biological and physical sciences and desire to continuously learn-as-you-go.</li></ul>With competitive salaries and a generous benefits package, we are widely considered to be one of the technology world’s most desirable employers. We have some of the most forward-thinking and hardworking people in the world working for us and, due to unprecedented growth, our exclusive engineering teams are rapidly growing. If youre a creative and autonomous engineer with a real passion for technology, we want to hear from you.Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 148,000 USD - 235,750 USD for Level 3, and 184,000 USD - 287,500 USD for Level 4.<div><div><div><div><div><div><div>You will also be eligible for equity and <a href="https://www.nvidia.com/en-us/benefits/" target="_blank">benefits</a>.</div></div></div></div></div></div></div>Applications for this job will be accepted at least until September 29, 2025.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.#deeplearning