the night before GTCNVIDIA’s annual AI conference, and after a day helping teach a workshop on large-scale language models, Sydney Knowles received a message that changed the course of her week. NVIDIA is hiring employees to help support build a clawis an event focused on giving developers hands-on experience with new AI tools.
What started as a two-hour volunteer slot has turned into something more like a week-long front row seat to the future of AI development. As soon as she stepped into the event tent, Knowles quickly realized she was in her element. That means acting fast and helping others work faster and smarter.
“Within NVIDIA, the AI work is moving fast and the tools are evolving rapidly,” said Knowles, a machine learning engineer on the IT organization’s Enterprise AI and Automation team. This team builds internal AI tools, tests NVIDIA technology in real employee workflows, and helps turn early ideas into systems that other teams can use to work faster and solve problems in new ways.
“The team’s goal is to use NVIDIA products to build tools that help employees in their daily lives,” she said.
This effort reflects NVIDIA’s philosophy of using our products internally, quickly learning from what works, and feeding those lessons back to our product teams.
This makes Knowles’ role more than a traditional engineering job. It’s also platform building, product feedback, and internal realization.
Deliver AI solutions company-wide
Since joining NVIDIA’s Enterprise AI and Automation team as an undergraduate intern in 2022 and returning as a full-time employee after graduation in 2023, Knowles has seen the scope of the team grow and change, keeping pace with the rapid evolution of AI.
What started as a small project has expanded to a portfolio that includes dozens of initiatives at a time, whether it’s developing tools for specific business functions, experimenting with early product features, or providing feedback to product teams before those technologies reach customers.
Knowles primarily works on employee productivity tools such as agent-based AI-powered personal assistants and AI chatbots for corporate intranets, but the broader team supports projects ranging from IT support to supply chain optimization.
Work often begins with actual internal needs. Your finance, human resources, marketing, corporate communications, or operations team might propose a proof of concept and ask for help turning it into something more durable.
Knowles’ group also sees opportunities to test new NVIDIA products for internal use cases. For example, an intranet AI project is NVIDIA’s Search extension generation researched and became a testing ground for data flywheel Concepts available to customers through NVIDIA NeMo platform.
But Knowles’ team isn’t looking to build all the solutions themselves. For companies with large technical teams, the bigger opportunity is often enablement.
“Within my team, we often want to help other teams build solutions for the long term,” says Knowles.
That philosophy is evident in the in-house personal assistant platform that the team designed to enable NVIDIA Group to connect its own agents and workflows to shared interfaces, skills, and automation.
The idea, says Knowles, is to “build a bunch of foundational building blocks and get them out of the way as quickly as possible.”
It’s a small group, so you’re close to the users. When employees raise issues or ideas in Slack, engineers often respond directly within minutes.
The pace is tough, but Knowles believes it’s part of the moment.
“As things have sped up, things have become more chaotic, but that chaos is part of the fun,” Knowles said.
Automate tasks and innovate
While AI can automate repetitive tasks and give people more room to think, it doesn’t ultimately decide what’s important, and human judgment is more important than ever, Knowles explains.
“AI will be able to say more in many ways,” she said. “You still have to mean what you say.”
This distinction runs through Knowles’ view of his work. The goal is not automation per se. This gives people more time to understand problems, separate signal from noise, and be creative where it matters.
One of the most rewarding parts of the job is how many teams Knowles is able to collaborate with and empower, from healthcare and life sciences to infrastructure and field organizations.
“Some of the smartest people on the planet work at NVIDIA,” Knowles says. “And I can help make their work possible.”
