Solutions Architect, LLM Model Builder

About the role

The Solutions Architect, Foundation Models will serve as a strategic technical expert and hands-on advisor for partners focusing on building, benchmarking, fine-tuning, optimizing, and deploying foundation model solutions for customer workloads.

Responsibilities

• Serve as the lead technical advisor for partners delivering reasoning, multimodal, fine-tuning, and model-serving solutions.
• Guide partners to the right approach for customer workloads across fine-tuning, distillation, quantization, compression, benchmarking, and evaluation.
• Define benchmark plans, synthetic data and evaluation workflows, and repeatable validation recipes.
• Advise on compute planning, including cluster sizing, GPU and network selection, storage, memory tradeoffs, latency and throughput targets, and production-readiness testing.
• Guide inference architecture across prefill and decode tradeoffs, batching, routing, disaggregated inference, and serving efficiency.
• Develop reference architectures, playbooks, benchmark recipes, TCO calculators, and sizing models across CUDA, NeMo, Nemotron, Dynamo, TensorRT-LLM, Triton, NIMs, and related tooling.
• Support pre- and post-sales engagements by translating complex model and infrastructure topics for partner and customer teams.

Requirements

• MSc, PhD in Computer Science, Electrical Engineering, Software Engineer, ML Engineer, or related fields (or equivalent experience).
• 5+ years of relevant experience working with LLMs, VLMs, and large-scale inference systems, with hands-on expertise in fine-tuning, benchmarking, evaluation, optimization, and production deployment as a Research Engineer, Deep Learning Engineer, or equivalent.
• Strong understanding of foundation models across data preparation, fine-tuning, post-training, evaluation, and inference.
• Familiarity with reasoning models, reinforcement learning, and synthetic data generation and evaluation workflows.
• Strong programming skills in Python and hands-on experience with PyTorch, JAX, or TensorFlow.
• Familiarity with Nemotron, NeMo, Dynamo, TensorRT-LLM, Triton, vLLM, and similar inference and optimization stacks.
• Strong communication and presentation skills, with the ability to advise both technical teams and executives.

Qualifications

• Experience helping partners or customers deploy large-scale AI systems in production.
• Built benchmark suites, fine-tuning recipes, sizing calculators, or TCO models for AI workloads.
• Strong knowledge of GPU infrastructure, including NVLink, InfiniBand, MPI, NCCL, or adjacent cluster technologies.
• Active OSS contributions in model tooling, inference, evaluation, or performance optimization.
• Comfortable moving between deep technical reviews, architecture guidance, benchmarking, and partner enablement.

Skills

• Strong understanding of foundation models and their applications.
• Expertise in fine-tuning, benchmarking, and optimization techniques.
• Knowledge of large-scale inference systems and their deployment strategies.
• Proficiency in Python and related frameworks.
• Experience with GPU infrastructure and cluster technologies.
• Ability to communicate complex technical concepts effectively.

Benefits

Base salary will be determined based on location, experience, and the pay of employees in similar positions. The base salary range is 152,000 USD - 241,500 USD. Eligible for equity and benefits.

Pay

Base salary will be determined based on location, experience, and the pay of employees in similar positions. The base salary range is 152,000 USD - 241,500 USD.

Schedule

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.