Solutions Architect, LLM Model Builder

Description

The company is hiring a Solutions Architect for Foundation Models to work with partners on reasoning models, multimodal models and production inference.

The role combines strategic technical leadership with hands-on advising. You will help partners build, benchmark, fine-tune, optimize and deploy foundation model solutions for their customers' workloads.

The Partner Solutions Architecture team is a trusted advisor across the partner ecosystem. It helps partners turn customer requirements into architectures, benchmark recipes, cluster test plans, compute sizing and production readiness, shortening time to value on the full-stack accelerated computing platform.

Responsibilities

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

Requirements

• Master's or PhD in computer science, electrical engineering, software engineering, machine learning engineering or a related field, or equivalent experience.
• 5+ years working with LLMs, VLMs and large-scale inference systems, including hands-on fine-tuning, benchmarking, evaluation, optimization and production deployment, as a research engineer, deep learning engineer or in an equivalent role.
• Solid understanding of foundation models across data preparation, fine-tuning, post-training, evaluation and inference.
• Working knowledge of reasoning models, reinforcement learning, and synthetic data generation and evaluation workflows.
• Strong Python skills 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, able to advise both technical teams and executives.
• Nice to have: experience helping partners or customers deploy large-scale AI systems in production.
• Nice to have: experience building benchmark suites, fine-tuning recipes, sizing calculators or TCO models for AI workloads.
• Nice to have: strong knowledge of GPU infrastructure, such as NVLink, InfiniBand, MPI, NCCL or adjacent cluster technologies.
• Nice to have: active open-source contributions in model tooling, inference, evaluation or performance optimization.
• Nice to have: comfort moving between deep technical reviews, architecture guidance, benchmarking and partner enablement.

Conditions

Base salary is set by location, experience and other objective job-related factors, within an expected range of 152,000 USD to 241,500 USD.

Applications are accepted at least until November 3, 2026.

This is an existing vacancy.

The company uses AI tools in its recruiting process.

The company is an equal opportunity employer and does not discriminate on protected characteristics.

Benefits

• Competitive salary and a comprehensive benefits package, with details available on the company's benefits site.