- Bachelor's, Master's, or PhD in Computer Science, Engineering, Machine Learning, or a related field, or equivalent experience.
- 8+ years in ML or software engineering, applied AI, FDE, or solutions architecture, including 3+ years of hands-on LLM post-training and taking models to production.
- Solid fundamentals in deep learning and machine learning. Deep expertise in post-training methods, including LoRA/PEFT, SFT, and reinforcement learning, with an understanding of the trade-offs between RL synchronous and asynchronous approaches. Strong grasp of model architectures and hyperparameters and how they shape training outcomes.
- Good understanding of large-scale distributed training and inference performance optimization. Ability to configure and tune workloads for memory efficiency, throughput, latency, and scaling across multi-GPU and multi-node systems.
- Hands-on experience with post-training data, including synthetic data pipelines, dataset curation and filtering, and evaluation sets. Experience with harness engineering for LLM and agentic systems. Proficiency in PyTorch, Hugging Face Transformers, Python, and Linux.
- Proven ability to scope an engagement, shape an SOW, and lead delivery with partners and customers. Excellent organizational skills, empathy for partner and customer needs, and the ability to drive complex decisions across teams and companies through trusted relationships.
- Clear communication of complex technical ideas, from engineers to C-level executives. Strong problem-solving skills, intellectual curiosity, and a commitment to partner and customer success.
Ways to Stand Out from the Crowd
- A hands-on technical expert and problem solver who can clearly describe solution paths for complex challenges.
- History of enabling partners and customers to successfully adopt open models and NVIDIA technologies and platforms.
- A technical vision that influences product and engineering decisions and drives production outcomes.
- Hands-on experience with Nemotron, Cosmos, and other open models, as well as both serverless and self-managed post-training platforms (e.g. Tinker APIs), including open-source frameworks such as NVIDIA NeMo and Megatron.
- Experience with multimodal or world-foundation-model post-training, and running training jobs on large GPU clusters with SLURM or Kubernetes.
Your base salary will be determined based on your location, experience, and other objective job-related factors. The expected base salary ranges for this position are: 184,000 USD – 287,500 USD for Level 4; and 224,000 USD – 356,500 USD for Level 5.
You will also be eligible for equity and benefits.
Applications for this job will be accepted at least until November 9, 2026.
This posting is for an existing vacancy.
NVIDIA uses AI tools in its recruiting processes.