• Operational sales activities in international channel / distribution sales • Building relationships with distributors • Placing products in the portfolio • Developing concrete business opportunities • Influencing the further development of the distribution model

Role Description We are in search of a curious and motivated Senior Solutions Architect to join our NVIDIA Infrastructure Specialists team. In this capacity, you'll support the creation, implementation, and verification of AI factories, focusing on running and debugging AI/LLM workloads and benchmarks on Linux-based GPU clusters. You'll engage with NCCL and collectives like AllReduce and AllToAll to boost performance and scalability, receiving mentorship from senior architects on the team. You will apply observability and automation to improve our benchmarking and validation efforts. You will be a key contact for troubleshooting workloads and benchmarks that fail, hang, or perform poorly. Additionally, you will collaborate with various NVIDIA teams to prepare AI factories for customers, validating both hardware and software for current AI applications. What You Will Be Doing: - Set up, adjust, and verify AI factory environments across multi-GPU and multi-node Linux clusters. - Validate configurations against guidelines for NCCL, collectives, and distributed training frameworks. - Run key AI/LLM benchmarks — setup, orchestration, result collection, and analysis. - Investigate and address problems when training jobs or benchmarks fail, hang, or perform below expectations. - Build and improve observability for AI factories (metrics, logs, traces, dashboards) to understand workload behavior and system health. - Build automation using Python and Shell for conducting benchmarks, retrieving results, and completing regression checks. - Analyze communication patterns and NCCL usage for AI/LLM workloads, concentrating on collectives such as AllReduce and AllToAll. - Help identify and recommend improvements to job configuration, parallelism strategies, and cluster settings to improve throughput, latency, and scaling efficiency. - Work closely with hardware, software, networking, and product teams to prepare AI factories for customer use. - Contribute to documentation and readiness materials for internal and customer-facing teams. Qualifications - Bachelor's degree or equivalent experience in Computer Science, Mathematics, Engineering, Physics, or a related field. - 5+ years of experience managing Linux-based systems in HPC, distributed systems, or AI/ML environments. - Hands-on experience running AI/ML workloads on multi-GPU and/or multi-node clusters, including some exposure to NCCL. - Practical knowledge of collective communication patterns like AllReduce and AllToAll, and their application in ML/LLM training. - Skilled in Python and Shell/Bash for scripting, automation, and tooling. - Strong communication skills and the ability to work effectively with cross-functional teams. Requirements - Experience benchmarking distributed systems — crafting, running, and interpreting performance benchmarks. - Background in HPC performance engineering, SRE, or systems performance analysis for GPU-accelerated environments. - Familiarity with observability stacks (metrics/monitoring, logging, tracing) used for large distributed systems. - Experience building automation and CI-style pipelines for running and validating benchmarks at scale. - Demonstrated interest in using AI to solve practical problems, improve workflows, and guide data-driven decisions. Benefits - Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 152,000 USD - 241,500 USD for Level 3, and 184,000 USD - 287,500 USD for Level 4. - You will also be eligible for equity and benefits. Company Description NVIDIA uses AI tools in its recruiting processes. NVIDIA is committed to fostering an inclusive 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.

• Own the clinical design of your deployment, end to end. From workflow discovery, through configuration, testing, and go-live. You define what "clinically correct" means for the engagement, then hold the line on it. • Turn clinical reality into a buildable plan. This is the core craft of the role. You'll take a tangle of medical policy, guideline sets, nurse workflows, and MD escalation rules and decompose it into clear requirements and acceptance criteria that can be handed directly to engineering. • Map workflows nobody has written down. Health plans rarely have an accurate picture of their own review process. You'll sit with their nurses, build the process diagram that reflects what actually happens, and use it to show where Florence plugs in. • Go deep into the details. Utilization management and prior authorization, guideline sets, lines of business, delegated entities, and the regulation coming down the pipe. You'll spend real time onsite with our customers building the kind of expertise you can't get from a deck. • Deliver proofs of concept fast. You'll know Florence deeply enough to configure and demonstrate real clinical value in an early engagement, not promise it for later. • Be a trusted advisor. The best Clinical Solutions Architects don't just deliver what customers ask for — they help clinical leaders understand what they actually need and guide them on how best to get there.

**The Role**This is a foundational technical hire at Nash, where we’re using AI models and agents to solve complex, real-world operational problems at enterprise scale. It’s a true Solutions Consulting role, combining traditional sales engineering with value consulting. You’ll carry a number, but you’ll earn it by helping customers work through both sides of the decision: how Nash will fit into their operation and why the investment is worth making. We’re building out our Solutions Consulting function and need someone who goes beyond running a strong demo. You’ll understand how a customer’s fleets, fulfillment channels, workflows, and systems operate, then turn that into a tailored solution, technical design, and business case. That could mean mapping an integration, shaping a POC, modeling the financial impact, or presenting the recommendation to a CFO or COO. Our customers are large enterprises with complex operations, and their real challenges rarely surface cleanly in the first call. This role is about digging until you find the use case that matters, then working in tight loops with the customer to design, prove, and sell the right solution. **What You'll Own** - **Uncover the real pain points and use cases:** Lead deep discovery with business and technical stakeholders to understand how the customer operates and where Nash can create the most value. - **Design the solution with the customer:** Turn discovery into a tailored solution, moving quickly through cycles of scoping, architecture, demos, testing, and refinement. - **Map how Nash fits into the customer’s stack:** Understand how complex merchants operate across OMS, WMS, TMS, POS, carrier integrations, APIs, and data flows, then show exactly how Nash fits. - **Run the sales engineering motion end to end:** Own demos, trials, and POCs from discovery through technical validation, executive alignment, and a clear customer decision. - **Build and defend the business case:** Quantify impact using the customer’s own data and present a compelling case to CFOs, COOs, CEOs, and other economic buyers. - **Represent Nash as a credible advisor:** Build credibility with prospects and partners, guide complex decisions, and develop a strong point of view on where Nash creates the most value. - **Be first in:** Test new AI models, agent capabilities, and product features as they ship, and bring the strongest applications into active customer solutions. - **Experiment, then codify:** Refine discovery frameworks, solution designs, value models, demos, and executive narratives, then turn what works into repeatable approaches. - **Enable the rest of the team on value:** Share what you learn with the GTM team, and close the loop with Product and Engineering on recurring customer needs and opportunities. **What We're Looking For** - 4 to 6 years in solutions engineering, presales, technical account management, technical program management, or another customer-facing technical role. - Experience owning complex enterprise opportunities from first conversation through close. - A genuine talent for discovery: you ask the question behind the question and push beyond surface-level pain points. - Experience leading structured discovery sessions or workshops that produce clear use cases, requirements, and success criteria. - Hands-on comfort with AI tooling, including actively testing new models and applying them to real customer problems. - Fluency in APIs, webhooks, system integrations, data flows, and enterprise architecture. - Strong financial and analytical skills, including experience building ROI models and defending the assumptions behind them. - Executive presence and the ability to move comfortably between business cases with senior leaders and technical discussions with engineers. - Strong communication and relationship-building instincts, with the ability to operate effectively in ambiguity. **Even better if you have:** Experience in value engineering, management consulting, technical program management, enterprise AI deployments, logistics, delivery, or supply chain. **Compensation & Benefits** - Competitive base salary + variable compensation (OTE aligned with top-quartile SaaS benchmarks). - Equity in a fast-growing venture-backed company. - Health, dental, vision, and 401(k). - Flexible PTO and remote-first culture.