Solutions Architect, Open Models

We’re hiring a Solutions Architect to join our AI Software Segment Solutions Architecture team, focused on open-weight AI models. In this role, you’ll support customer-facing technical engagements and help connect real-world workloads to NVIDIA’s hardware and software ecosystem. We’re looking for someone who is technically curious, eager to develop deep expertise in NVIDIA platforms, and excited to grow into a trusted technical resource for customers and partners. If you’re passionate about applied technology and want to learn from some of the best engineers in the world, we’d love to hear from you.

What You’ll Be Doing:

• Support technical engagements with customers and partners. helping to understand their workloads and identify how NVIDIA’s technology stack can address their needs, under the mentorship of senior team members.
• Participate in the design and validation of scalable architectures, assisting in the development of integration plans and deployment approaches for customer and partner solutions.
• Engage hands-on with technical topics—building, testing, and validating solutions that help customers and partners get started with and scale NVIDIA technologies.
• Help document and communicate customer and partner needs and technical feedback, supporting the relay of field insights to internal product and engineering teams.
• Collaborate with engineering and product teams to understand NVIDIA’s solutions and support successful customer deployments.
• Run end-to-end post-training programs with enterprise customers. This includes supervised fine-tuning, reinforcement learning (GRPO, DPO, PPO), LoRA / PEFT, data curation, synthetic data generation, reward design, and evaluation design.
• Build evaluation environments that give customers an objective measure of post-training progress. These environments feed field signal back to Product and Research, improving the next generation of NVIDIA’s open-weight models.
• Translate hands-on work into reusable recipes, reference pipelines, and playbooks. These let other NVIDIA teams, customers, and partners run post-training and evaluation themselves.

What We Need to See:

• Bachelor’s or Master’s degree or equivalent experience in Computer Science, Engineering, Mathematics, Physics, Data Science, or a related field.
• 2-5+ years of professional experience in software/hardware engineering, developer relations, technical partnerships, solutions architecture, or a related technical field — with some hands-on exposure to open-weight model customization and the AI/ML systems ecosystem.
• Foundational technical knowledge in AI/ML, particularly post-training, reinforcement learning, LoRA / PEFT, data curation, and evaluation, with a proven curiosity about building expertise and contributing to technical solutions.
• Ability to communicate technical concepts clearly to both technical and non-technical audiences, with a desire to grow this skill across more complex topics and senior collaborator levels.
• Strong problem-solving skills, intellectual curiosity, and a genuine passion for aiding customers succeed with technology.
• Proven ability to form relationships with customers or partners, with a curiosity about developing the technical credibility and trust needed to support complex product decisions.
• Proficiency with agent-assisted model training workflows. Comfortable using AI agents such as Claude Code, Cursor, or Codex to build, debug, and iterate on training and evaluation pipelines. 3+ years of Linux experience, including comfort launching training and evaluation jobs on SLURM or Kubernetes.

Ways to Stand Out from the Crowd:

• A hands-on technical problem solver who can clearly articulate how technology works and apply that understanding to real customer challenges.
• Early experience supporting customers and partners in adopting new technologies or platforms, with a passion for enabling others to succeed.
• Experience with and/or contributions to open-source NVIDIA AI libraries and models, particularly Nemotron, NeMo, NeMo-RL, and NeMo Gym.
• Hands-on experience designing agent environments, synthetic data pipelines, and verifiable rewards for RL post-training, or using serverless post-training APIs such as Tinker.
• Hands-on experience customizing open-weight models (Nemotron, Qwen, DeepSeek, GLM, Kimi), or AI model training applied to multimodal data.

You will also be eligible for equity and benefits.

This posting is for an existing vacancy.

NVIDIA uses AI tools in its recruiting processes.