... fitness, learning, therapy, companions, customer experience and media; representing 100s of ... Our technology has powered experiences from companies such as NVIDIA, Microsoft Xbox, Niantic ...
... fitness, learning, therapy, companions, customer experience and media; representing 100s of ... Our technology has powered experiences from companies such as NVIDIA, Microsoft Xbox, Niantic ...
Nvidia Deep Learning information
See Pennsylvania salary details
$21.9K is the 25th percentile. Wages below this are outliers.
$11K - $22.8K
27% of jobs
$22.8K - $34.5K
0% of jobs
$34.5K - $46.3K
0% of jobs
$46.3K - $58K
0% of jobs
$58K - $69.8K
0% of jobs
The median wage is $80.6K / yr.
$69.8K - $81.6K
25% of jobs
$81.6K - $93.3K
18% of jobs
$101.7K is the 75th percentile. Wages above this are outliers.
$93.3K - $105.1K
7% of jobs
$105.1K - $116.8K
2% of jobs
$116.8K - $128.6K
0% of jobs
$128.6K - $140.3K
21% of jobs
$11K
$84.1K
$140.3K
How much do nvidia deep learning jobs pay per year?
What is an Nvidia Deep Learning job?
An Nvidia Deep Learning job typically involves working with AI, machine learning, and deep learning technologies to develop, optimize, and deploy neural network models. Employees in these roles may work on GPU acceleration, AI frameworks like TensorFlow and PyTorch, and specialized hardware like NVIDIA GPUs and TensorRT. Positions can range from research scientists and software engineers to AI infrastructure specialists, focusing on improving model performance and scalability. These professionals contribute to cutting-edge AI applications in fields like autonomous vehicles, healthcare, and robotics.
What are the main challenges faced by professionals working in Nvidia Deep Learning roles?
Professionals in Nvidia Deep Learning positions often encounter challenges such as optimizing deep learning models to run efficiently on GPU architectures, keeping up with rapidly evolving AI frameworks, and troubleshooting complex system-level integration issues. They may also need to balance tight project deadlines with the demands of rigorous research and experimentation. Collaboration with interdisciplinary teams—such as software developers, data scientists, and hardware engineers—is common and essential to deliver robust solutions. Overcoming these challenges helps professionals stay at the forefront of innovation in the AI and deep learning industry.
What are the key skills and qualifications needed to thrive in the Nvidia Deep Learning position, and why are they important?
Excelling in an Nvidia Deep Learning role requires a strong background in computer science, machine learning, and mathematics, often supported by an advanced degree in a related field. Expertise in deep learning frameworks (such as TensorFlow or PyTorch), CUDA programming, and experience with Nvidia GPU hardware are typically expected, along with relevant certifications like Nvidia Deep Learning Institute credentials. Strong analytical thinking, problem-solving abilities, and effective teamwork distinguish top performers in this position. These skills are crucial to efficiently develop, optimize, and deploy deep learning models leveraging Nvidia technologies in cutting-edge applications.

Staff / Principal Research Scientist - Switzerland
Indiana, PA • On-site
Other
Re-posted 3 days ago
Job description
Inworld is a research lab of top researchers and engineers, building the world’s top-ranked realtime voice models.
Today our models are the #1 ranked realtime voice models in the world. They are used to power the largest consumer-facing AI applications available, across categories like health, fitness, learning, therapy, companions, customer experience and media; representing 100s of millions of end users. Our work spans areas like research and development of state-of-the-art models, optimizing realtime inference, and creating best-in-class APIs and products that allow developers to engage their users.
We’ve raised more than $125M from Lightspeed, Section 32, Kleiner Perkins, Microsoft’s M12 venture fund, Founders Fund, Meta and Stanford, among others. Our technology has powered experiences from companies such as NVIDIA, Microsoft Xbox, Niantic, Logitech Streamlabs, Wishroll, Little Umbrella and Bible Chat. We’ve also been recognized by CB Insights as one of the 100 most promising AI companies globally and have been named one of LinkedIn’s Top 10 Startups in the USA.
Who We're Looking ForA year ago, reliably working agentic systems barely existed. Nobody has a decade of experience here. So we're not screening for a resume template — we're looking for strong people from varied backgrounds who learn fast, thrive in ambiguity, and can show us what they've built, broken, and understood.
Experience We Find UsefulYou don't need all of this. But you need enough to make a case.
- Foundation models: training, new architectures, RL, reward modeling, scaling
- Evaluation: benchmarks, eval loops, quality measurement, LLM-as-judge, failure analysis
- Frontier topics: multimodal models, agents, tool use, test-time compute, world models
- Published research at ICML, ICLR, NeurIPS, EMNLP, ACL, or AAAI
- PhD in ML/NLP — or equivalent practical experience you can point to
- Public work: non-trivial AI side projects, interdisciplinary experiments, open-source contributions
- Full-stack research ownership: you frame the question, run the experiments, write the paper, ship the result
- Professional fluency in English (written and spoken) is required, as you will be collaborating daily with our US-based leadership and engineering teams.
If you learned through building, competitions, or collaborations outside academia, that counts. We care about evidence, not credentials.
Who Thrives Here- You don’t need a roadmap to start walking; you’re comfortable picking a direction and building the map as you go
- You believe research isn't finished until it’s shipped. You have a bias for impact over purely academic output
- You don't just ship code; you obsess over the why. You’re the first to question an approach if you think there’s a better way to solve the core problem
- You aren’t satisfied with "the PM said so." You thrive on deep context and want to understand the fundamental logic behind every decision we make
We hand you unclear problems and expect you to make them clear. We value researchers who say "I don't know yet" and then design the experiment that finds out. We treat evaluation as a first-class research product, not a box to check before launch. Impact comes before publications though we support sharing work that moves the field forward. Your work should be visible. Flat structure, fast iterations, minimal process theater.
Location & Employment- Location: remote within Switzerland
- Employment type: Full-time, permanent employment
- Hiring model: Employment via Employer of Record (EOR)
Candidates must already have the legal right to work in Switzerland, as visa sponsorship is not available for this role. For candidates interested in relocating to the San Francisco Bay Area in the future, full U.S. visa and relocation support may be available, subject to business needs and applicable legal and work authorization requirements.