TT-Fabric Software Engineer

As a TT-Fabric Software Engineer at Tenstorrent, your primary responsibilities include architecting and maintaining our low-level networking library (TT-Fabric) that enables efficient and reliable communication among thousands of AI processors. You'll design robust systems to handle network communications for distributed inference and training infrastructures, ensuring scalability and performance. Furthermore, you will tune performance and optimize protocols to maximize the effectiveness of AI model capabilities, all while collaborating with fellow engineers and AI researchers to integrate networking solutions into our programming model.

To apply for the TT-Fabric Software Engineer position at Tenstorrent, candidates should have a Bachelor's degree or higher in Computer Science, Electrical/Computer Engineering, or a closely related field. A strong foundation in C/C++ and systems programming is crucial, along with a passion for low-level programming and optimizing hardware-software interactions. While experience with high-performance networking libraries or large-scale computer clusters can be beneficial, it is not required. Overall, a curiosity for exploring innovative solutions and a willingness to tackle challenging problems is key.

Yes! Tenstorrent welcomes applicants of all seniorities for the TT-Fabric Software Engineer role. During the interview process, candidates will be evaluated to determine the appropriate level of the position and corresponding compensation package that reflects their experience and qualifications. This inclusivity makes Tenstorrent a perfect place for both experienced professionals and emerging talents eager to make an impact in the AI field.

The expected salary range for the TT-Fabric Software Engineer position at Tenstorrent spans from $100k to $500k, which includes both base and variable compensation targets. The final offer will depend on various factors, such as experience, skills, education, and location. This competitive compensation package reflects Tenstorrent's commitment to attracting top talent and fostering a stimulating work environment.

Tenstorrent provides a highly competitive compensation package and a range of benefits for its employees, especially for the TT-Fabric Software Engineer role. This includes flexible work options (hybrid model), health and wellness programs, professional development opportunities, and a collaborative team culture that nurtures creativity and innovation. As an equal opportunity employer, Tenstorrent fosters an inclusive environment, understanding that diverse perspectives drive the best results.

When designing a low-level networking library like TT-Fabric, focus on understanding the specific requirements of distributed systems, such as efficiency, scalability, and reliability. Begin by analyzing the hardware capabilities and how to best utilize them. Prioritize modular designs that allow for easy updates and optimizations, and conduct performance tests regularly to fine-tune your library. It's essential to collaborate with both hardware engineers and AI researchers to ensure the library aligns well with overarching objectives.

I have hands-on experience with performance tuning and protocol optimization in previous projects. My approach includes thorough profiling of networking code to identify bottlenecks, utilizing tools like profilers and benchmarks to gather data. I emphasize understanding the underlying hardware interactions and evaluating different algorithms to ensure optimum throughput and latency. I also prioritize continuous integration of performance testing within development cycles to catch issues early.

Understanding low-level programming is crucial for the TT-Fabric Software Engineer role because it allows for better control over hardware-software interactions, ensuring that applications run efficiently on the given infrastructure. In this position, optimizing performance might involve fine-tuning memory usage, managing CPU cores, or leveraging specific hardware features. A strong grounding in low-level programming leads to creating higher-performing systems that can handle complex tasks reliably.

For a role focused on AI and HPC, protocols like RDMA (Remote Direct Memory Access) and MPI (Message Passing Interface) are particularly relevant. RDMA allows for efficient data transfer with minimal CPU overhead, which is vital for high-performance computing tasks. MPI is essential for enabling parallel processing across distributed systems. Understanding these protocols enables better design of communication systems among a cluster of processors.

To stay updated with the latest trends in low-level programming and networking, I regularly follow industry blogs, participate in online forums, and attend relevant conferences and workshops. I also engage with open-source projects and experiments with new techniques in my personal projects. Continuous learning is crucial, as it not only broadens my skill set but also sparks new ideas that can be applied in my professional work.

In a previous position, I encountered a significant bottleneck in network communication while scaling our services. I began by analyzing the system logs and network traffic to identify the root cause. I then implemented protocol optimizations that aligned better with our hardware capabilities. Collaborating with my team, we redesigned some of our messaging patterns to reduce overhead. This problem-solving approach not only resolved the bottleneck but also improved our overall system performance.

I'm particularly drawn to Tenstorrent because of its innovative approach to AI and its commitment to high performance. The opportunity to work on cutting-edge technology that is evolving how we practice computing is incredibly appealing. Additionally, the collaborative culture at Tenstorrent resonates with me, as I believe teamwork drives the best outcomes. I'm eager to contribute my skills while learning from other passionate engineers.

I manage my time during complex networking projects by breaking down tasks into smaller, manageable components and setting clear milestones. I utilize project management tools to prioritize my work and ensure efficient use of resources. Regular check-ins with my team help keep progress aligned with our objectives, and I build in time for testing and optimization phases to refine our solutions further.

When working with large-scale computer clusters, it's crucial to consider factors such as resource allocation, load balancing, and fault tolerance. Effective communication protocols must ensure seamless data transfer among all nodes, while system architecture should facilitate scalability. Monitoring tools are also important to track performance and detect issues. Properly addressing these considerations leads to reliable and efficient operations.

If I encountered a security issue in a networking application, the first step would be to assess the impact and determine the extent of the vulnerability. I'd gather relevant logs and metrics for analysis to identify the root cause. Next, I would implement a temporary fix if needed, while collaborating with the team to formulate a long-term solution. Communication with relevant stakeholders is vital to ensure that everyone understands the issue and steps taken to mitigate it, followed by rigorous testing before redeploying the systems.