Working Student - LLM Inference Performance

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About Tensordyne 
AI is transforming our world. It can perform cognitive functions that previously only humans could do, such as perceiving interactions across different modalities and environments - with the ability to quickly learn and then solve complex problems. Tensordyne is an AI system solution company that builds very high-performance, low-power generative AI inference systems. Our mission, through the creation of custom silicon, hardware and software, is to enable multimodal Generative AI inference acceleration at scale, with safe, sustainable, high-performance systems for our hyperscaler and neocloud data center customers. We are at the leading edge of advancing the latest research and product improvements for generative Al inference solutions that will make Al even more advantageous for compelling new generative AI applications.Tensordyne is a well funded, fast-paced startup company with headquarters in both Sunnyvale, CA, and Munich, Germany. We also have many talented team members working remotely across North America and Europe. We take care of our people and their families with comprehensive benefits, competitive compensation, flexible spending options, and recognition programs, because building category-defining technology starts with a healthy, supported team. Come join us as we shape the future of multimodal generative artificial intelligence!

About the Role
How fast should an LLM building block run on our hardware? How fast does it actually run, and what would make it faster?

As a Working Student, LLM Inference Performance, you'll help answer these questions by working directly with our hardware and software engineering teams.

You'll write and optimize low-level kernels, benchmark performance on real silicon, and develop profiling tools that help engineers understand and improve LLM inference performance.

You'll gain hands-on experience with our Rust programming stack and Python SDK, use AI coding agents as part of your development workflow, and collaborate closely with engineers working on compilers, runtimes, and kernel optimization.

This is an opportunity to apply your academic knowledge to real-world AI hardware challenges while taking ownership of meaningful technical projects.

What You'll Do

Understand our hardware: Learn how Tensordyne silicon processes data, manages memory, and synchronizes operations, and how key LLM components such as attention, MLP, and Mixture-of-Experts (MoE) routing map onto the architecture..

Develop and optimize kernels: Implement, test, and tune low-level kernels to improve the performance of LLM inference workloads..

Analyze performance: Build analytical and roofline performance models, compare predictions against simulator and real-silicon measurements, and investigate performance bottlenecks..

Build benchmarking and profiling tools: Develop benchmark harnesses, profiling scripts, and trace visualizations that make performance easier to measure, understand, and improve..

Work with AI coding agents: Explore workflows where AI agents assist with kernel development, optimization, and benchmarking, and identify the tools and context they need to work effectively..

Collaborate across engineering teams: Work closely with compiler, runtime, and kernel engineers to translate performance insights into practical improvements..

Example project: Develop a benchmarking and profiling harness for an LLM building block, such as decode attention, at two abstraction levels. Compare measured performance against theoretical estimates and make the tooling accessible to AI coding agents.

What We're Looking For

Currently enrolled in a Master's degree program in Computer Science, Electrical Engineering, Mathematics, or a related technical field..

Strong programming skills in Python and at least one of C, C++, or Rust..

Understanding of computer architecture, memory systems, bandwidth, and parallel computing through academic coursework or personal projects..

Interest in low-level programming, hardware performance, and optimization..

Practical experience using AI coding agents in development workflows, with the ability to evaluate and improve their output..

An experimental mindset, with the ability to form hypotheses, analyze measurements, and communicate technical findings clearly..

Curiosity, initiative, and enthusiasm for learning new technologies..

Nice to Have
Experience in one or more of the following areas would be a plus:

GPU or AI accelerator programming..

Embedded systems or bare-metal programming..

Performance profiling, benchmarking, or optimization..

LLM inference fundamentals and architectures..

Why Join Tensordyne?

Work on cutting-edge AI technology: Contribute to the development of next-generation AI inference hardware and software..

Gain hands-on experience: Apply your university knowledge to real silicon and challenging engineering problems..

Own meaningful projects: Build tools and solutions that directly support our engineering teams..

Learn from experienced engineers: Work alongside specialists in hardware architecture, compilers, runtimes, and performance optimization..

Explore AI-assisted development: Gain practical experience building with AI coding agents and shaping how they interact with advanced hardware systems..

Make a lasting impact: Help develop performance tools and workflows that will support both current and future generations of Tensordyne technology..

Tensordyne's culture was built on the following values

Put people first. We only succeed when our people succeed.

Ethics and integrity always. Be open, honest, and respectful of everyone.

Think Big. Be ambitious and have audacious goals.

Aim for excellence. Quality and excellence count in everything we do.

Own it and get it done. Results matter!

Make each person better together than they would be as an individual.

Embrace each other's differences, and embrace that there will be differences.

Tensordyne is an equal opportunity employer. We believe diverse teams are better equipped to solve complex problems and build exceptional technology. All qualified applicants will receive consideration for employment without regard to age, color, gender identity or expression, marital status, national origin, disability, protected veteran status, race, religion, pregnancy, sexual orientation, or any other characteristic protected by applicable laws, regulations, and ordinances.

A note to recruitment agencies: Please do not contact Tensordyne employees or leaders regarding this role. We do not accept unsolicited agency resumes and are not responsible for fees associated with unsolicited submissions.

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关于 Tensordyne
人工智能正在改变我们的世界。它能执行过去只有人类才能完成的认识功能，例如感知不同模态和环境中的交互 - 具备快速学习然后解决复杂问题的能力。Tensordyne 是一家 AI 系统解决方案公司，构建高性能、低功耗的生成式 AI 推理系统。我们的使命是通过创建定制化的硅、硬件和软件，为我们的超大规模和新云计算数据中心客户提供安全、可持续、高性能的系统，实现大规模多模态生成式 AI 推理加速。我们处于推进最新研究和产品改进的前沿，这些改进将使 AI 对于引人入胜的新生成式 AI 应用更加有利。Tensordyne 是一家资金充足、快速发展的初创公司，总部位于美国加利福尼亚州森尼韦尔和德国慕尼黑。我们还有许多才华横溢的团队成员在北美和欧洲远程工作。我们通过全面的福利、有竞争力的薪酬、灵活的支出选择和认可计划来照顾我们的员工及其家人，因为开创类别的技术始于健康、受支持的团队。加入我们，共同塑造多模态生成式人工智能的未来！

关于这个职位
在我们的硬件上，LLM 构建块应该运行多快？它实际运行多快，什么能让它更快？

作为一名 LLM 推理性能的实习学生，你将帮助回答这些问题，直接与我们的硬件和软件工程团队合作。

你将编写和优化低级内核，在真实硅芯片上进行性能基准测试，并开发分析工具，帮助工程师理解和改进 LLM 推理性能。

你将获得使用我们的 Rust 编程栈和 Python SDK 的实践经验，将 AI 编码代理作为开发工作流程的一部分，并与从事编译器、运行时和内核优化的工程师紧密合作。

这是一个将你的学术知识应用于实际 AI 硬件挑战的机会，同时拥有有意义的技术项目所有权。

你的工作内容

了解我们的硬件：学习 Tensordyne 硅片如何处理数据、管理内存和同步操作，以及关键 LLM 组件（如注意力机制、MLP 和专家混合（MoE）路由）如何映射到架构上。

开发和优化内核：实现、测试和调整低级内核，以提高 LLM 推理工作负载的性能。

分析性能：构建分析和性能上限模型，将预测与模拟器和真实硅芯片测量结果进行比较，并调查性能瓶颈。

构建基准测试和分析工具：开发基准测试工具、分析脚本和跟踪可视化，使性能更容易测量、理解和改进。

使用 AI 编码代理：探索 AI 辅助内核开发、优化和基准测试的工作流程，并确定它们有效工作所需的工具和上下文。

跨工程团队协作：与编译器、运行时和内核工程师紧密合作，将性能见解转化为实际改进。

示例项目：为 LLM 构建块（如解码注意力机制）开发两个抽象级别的基准测试和分析工具。将测量的性能与理论估计进行比较，并使这些工具对 AI 编码代理可用。

我们正在寻找

目前就读于计算机科学、电气工程、数学或相关技术领域的硕士课程。

具备 Python 编程技能，以及 C、C++ 或 Rust 中至少一种的熟练技能。

通过学术课程或个人项目了解计算机体系结构、内存系统、带宽和并行计算。

对低级编程、硬件性能和优化有兴趣。

在实际开发工作流程中使用 AI 编码代理的实践经验，能够评估和改进其输出。

具有实验心态，能够形成假设、分析测量结果并清晰传达技术发现。

好奇心、主动性和学习新技术的热情。

加分项
以下一个或多个领域的经验将是加分项：

GPU 或 AI 加速器编程。

嵌入式系统或裸机编程。

性能分析、基准测试或优化。

LLM 推理基础和架构。

为什么加入 Tensordyne？

从事尖端 AI 技术：为下一代 AI 推理硬件和软件的开发做出贡献。

获得实践经验：将你的大学知识应用于真实硅片和具有挑战性的工程问题。

拥有有意义的项目：构建直接支持我们工程团队的工具和解决方案。

向经验丰富的工程师学习：与硬件架构、编译器、运行时和性能优化方面的专家一起工作。

探索 AI 辅助开发：获得使用 AI 编码代理构建并塑造它们与高级硬件系统交互方式的实践经验。

产生持久影响：帮助开发支持当前和未来几代 Tensordyne 技术的性能工具和工作流程。

Tensordyne 的文化建立在以下价值观上

以人为本。只有当我们的员工成功，我们才能成功。

永远保持道德和诚信。对每个人都要开放、诚实和尊重。

敢于梦想。要有雄心壮志和大胆的目标。

追求卓越。我们所做的一切都要注重质量和卓越。

负责并完成它。结果很重要！

让每个人在一起比单独变得更好。

彼此接纳差异，并接受差异的存在。

Tensordyne 是一个平等机会雇主。我们相信更多样化的团队能更好地解决复杂问题和构建卓越的技术。所有符合条件的申请者都将获得就业考虑，而不受年龄、肤色、性别认同或表达、婚姻状况、国籍、残疾、受保护的退伍军人身份、种族、宗教、怀孕、性取向或适用法律、法规和条例保护的其他任何特征的影响。

致招聘机构的说明：请勿就此职位联系 Tensordyne 员工或领导层。我们不接受主动提交的简历，对主动提交产生的费用不承担责任。

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