Solutions Architect, Open Models

NVIDIA

. Support technical engagements with customers and partners to understand workloads and identify how NVIDIA’s technology stack can address their needs, under senior-team mentorship.

Posted 11/10/2026full-timeRemote • California • United StatesJuniorMid-Level💰 $124,000 - $241,500 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in AI/ML systems, particularly in post-training methodologies, reinforcement learning, and model customization. Proficient in building and validating scalable architectures while effectively communicating technical concepts to diverse audiences.

Highest-signal resume keywords

AI/ML Systems EcosystemReinforcement LearningLinux ExperienceOpen-Weight Model CustomizationAgent-Assisted Model Training Workflows

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Post-Training MethodologiesData CurationEvaluation DesignSynthetic Data GenerationModel CustomizationTechnical PartnershipsSolutions ArchitectureProblem-SolvingTechnical CommunicationCustomer Engagement

Soft Skills

Intellectual CuriosityRelationship BuildingTrust Development

Tools & Technologies

SLURMKubernetesClaude CodeCursorCodexNVIDIA AI LibrariesNemotronNeMoNeMo-RLNeMo Gym

Industry Keywords

Computer ScienceEngineeringMathematicsPhysicsData ScienceTechnical EngagementsCustomer DeploymentsEnterprise Customers

Tech Stack

Tools & technologies

KubernetesLinux

About the role

Key responsibilities & impact

Support technical engagements with customers and partners to understand workloads and identify how NVIDIA’s technology stack can address their needs, under senior-team mentorship.
Participate in designing and validating scalable architectures, integration plans, and deployment approaches.
Build, test, and validate solutions that help customers and partners adopt and scale NVIDIA technologies.
Document and communicate customer and partner needs and technical feedback to internal product and engineering teams.
Collaborate with engineering and product teams to support successful customer deployments.
Run end-to-end post-training programs with enterprise customers, including supervised fine-tuning, reinforcement learning (GRPO, DPO, PPO), LoRA / PEFT, data curation, synthetic data generation, reward design, and evaluation design.
Build evaluation environments measuring post-training progress and feeding field signals to Product and Research.
Translate hands-on work into reusable recipes, reference pipelines, and playbooks for NVIDIA teams, customers, and partners.

Requirements

What you’ll need

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.
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.
Ability to communicate technical concepts clearly to technical and non-technical audiences.
Strong problem-solving skills, intellectual curiosity, and passion for aiding customers with technology.
Ability to form relationships with customers or partners and develop technical credibility and trust.
Proficiency with agent-assisted model training workflows.
Comfort 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 launching training and evaluation jobs on SLURM or Kubernetes.
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 such as Nemotron, Qwen, DeepSeek, GLM, and Kimi, or applying AI model training to multimodal data.

Benefits

Comp & perks

Equity
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