> Markdown version of [/jobs/ext/1250176-senior-research-engineer-enterprise-products](https://www.wearedevelopers.com/jobs/ext/1250176-senior-research-engineer-enterprise-products). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md).

---

# Senior Research Engineer - Enterprise Products

- **Company:** NVIDIA Ltd.
- **Location:** Washington, DC, United States
- **Experience:** Expert
- **Salary:** $101,535.0 - $155,000.0
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Artificial Neural Networks, Code Review, Distributed Systems, Machine Learning, Natural Language Processing, Open Source Technology, Tensorflow, System Software, Speech Recognition, Graphics Processing Unit (GPU), Pytorch, Large Language Models, Deep Learning, Generative AI, Information Technology
- **Published:** July 12, 2026
- **Apply:** https://www.careerjet.com/job/us069db52cc72ef920a8d76a75d5bfed37/eaa

## About the Role

Bachelor's of Master's degree in Computer Science or equivalent experience.
8+ years of industry experience in Deep Learning frameworks (PyTorch or TensorFlow).
Experience designing or running LLM evaluations/benchmarks - ideally agentic ones - and drawing statistically sound conclusions from them
Understanding of modern techniques in Machine Learning, Deep Neural Networks, Natural Language Processing, or Speech Recognition.
Empirical research mindset: forming hypotheses about new algorithms, running calibrations, iterating on results
Strong communication and interpersonal skills, along with the ability to work in a dynamic and distributed team. A history of mentoring junior engineers and interns is a huge plus.
A desire to constantly grow and learn new things.
Strong computer science fundamentals - algorithms and data structures, computational complexity, parallel and distributed computing, system software.
Ways to stand out from a crowd:
Experience architecting or developing large-scale distributed systems for deep learning.
Agentic benchmark creation and publications.
Knowledge of CPU and/or GPU architecture.

## Description

We are now looking for a Senior Research Engineer passionate about Generative AI inference. Are you excited to change the way people infuse AI into products and services? NVIDIA is at the forefront of generative AI models, from language to images. NVIDIA provides building blocks to democratize AI and make generative AI easy to develop, integrate, and deploy. Our team is dedicated to developing optimized inferencing technologies to support our growing generative AI needs. We contribute to all steps of the machine learning lifecycle: from conceptualization, to applied research, engineering for optimized inference, and deployment. Collaborate with research teams, engineers, and open-source community.
What you will be doing:
Design and evaluate routing policies for LLM traffic to best use mixture of model systems.
Build and run agentic benchmarks (e.g., Terminal-Bench ) to measure algorithm quality, and turn results into calibration data and routing profiles
Ship to an open-source repo: design docs, code review, docs, and community contributions
Collaborating with engineering teams across all of NVIDIA to ensure our software integrates seamlessly up and down the NVIDIA accelerated serving stack.


## Related Videos

- [Your Next AI Needs 10,000 GPUs. Now What?](https://www.wearedevelopers.com/videos/1590-your-next-ai-needs-10-000-gpus-now-what)
- [Are Code Reviews Worth It? Insights from 16 Years of Review Data](https://www.wearedevelopers.com/videos/1135-are-code-reviews-worth-it-insights-from-16-years-of-review-data)
- [Machine learning in the browser with TensorFlowjs](https://www.wearedevelopers.com/videos/155-machine-learning-in-the-browser-with-tensorflowjs)
- [Photonic Computing: Programming a New Class of AI Accelerators (incl. Live Coding)](https://www.wearedevelopers.com/videos/100196-photonic-computing-programming-a-new-class-of-ai-accelerators-incl-live-coding)
- [Making neural networks portable with ONNX](https://www.wearedevelopers.com/videos/301-making-neural-networks-portable-with-onnx)
- [Developing an AI.SDK](https://www.wearedevelopers.com/videos/198-developing-an-ai-sdk)

## Related Articles

- [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer)
- [What Are Large Language Models?](https://www.wearedevelopers.com/magazine/304-what-are-large-language-models)
- [MLOps And AI Driven Development](https://www.wearedevelopers.com/magazine/82-mlops-and-ai-driven-development)
- [Graph and AI Trends 2026: Why Is AI Running but Not Yet Delivering?](https://www.wearedevelopers.com/magazine/680-graph-and-ai-trends-2026-why-is-ai-running-but-not-yet-delivering)
- [MLOps – What’s the deal behind it?](https://www.wearedevelopers.com/magazine/125-mlops-what-s-the-deal-behind-it)
- [Best US AI Conferences for CTOs in 2026: Build vs. Buy, Vendor Evaluation, and Peer Intelligence](https://www.wearedevelopers.com/magazine/736-best-us-ai-conferences-for-ctos-in-2026-build-vs-buy-vendor-evaluation-and-peer-intelligence)