Distributed Software Engineer, Tenstorrent
$100-500k
Next-generation computing company building computers for AI
Open for applications
Next-generation computing company building computers for AI
1001+ employees
Open for applications
$100-500k
1001+ employees
Company mission
To bring unique solutions to the problems facing AI and machine learning.
Role
Who you are
- We welcome candidates at various experience levels for this role. During the interview process, candidates will be assessed for the appropriate level, and offers will align with that level, which may differ from the one in this posting
- Bachelor's degree or higher in Computer Science, Electrical/Computer Engineering, or a related field
- Solid proficiency in C/C++ and a foundational understanding of systems programming, operating systems, and distributed system principles
- Enthusiasm for distributed computing, including inter-process communication (IPC), socket programming, cluster resource management, and distributed inference and training
- Willingness to think from first principles, consider out-of-the-box solutions, discover where industry norms and current state-of-the-art fall short, and drive to surpass them
- Desire to learn, grow, and become an expert in distributed systems, bringing curiosity and innovative thinking to solve challenging problems in large-scale computing
- Experience or familiarity with high-performance networking, MPI, RDMA, or cluster computing frameworks is advantageous but not required
What the job involves
- As our TT-Distributed Software Engineer, you will develop and optimize distributed software systems that power the most efficient and highest-performing AI and HPC clusters
- In this role, you'll work on distributed programming across multiple nodes, utilizing systems programming, inter-node communication, and Tenstorrent’s scalable architectures to advance the state-of-the-art distributed inference and training infrastructure
- Architect, implement, and optimize distributed software systems (TT-Distributed) to efficiently manage communications and computations across clusters of AI accelerators and CPUs
- Design robust systems leveraging inter-process communication (IPC), inter-node sockets, and distributed programming frameworks like MPI to ensure reliability, scalability, and high performance
- Dive deep into system-level optimizations, cluster resource management (e.g., Slurm), and performance tuning to maximize efficiency in distributed AI workloads
- Collaborate closely with AI researchers and hardware engineers to integrate distributed inference and training frameworks into Tenstorrent’s broader software ecosystem
Our take
Tenstorrent is a US- and Canada-based semiconductor startup specializing in processors for AI workloads. Rather than building gneral-purpose CPUs or GPus, the company develops custom chips and sofrware optimized for running moder AI models. As demand for AI accelerates, and as a global chip shortage reshapes the semiconductor industry, these trends have become strong tailwinds for Tenstorrent.
The company’s unique approach is to build for this future software, which it believes will be written at a higher level. Combining this with conditional computing, Tenstorrent supports a scalable distributed network approach that is analogous to a human brain.
While the market for specialized AI hardware is destined to be gigantic, the company has to compete with a plethora of hopeful startups (as well as major tech companies like NVIDIA and Qualcomm). That said, Tenstorrent boasts an excellent trajectory so far, with its latest moves including unveiling its high-end Wormhole AI processors, and a partnership with LG which will see the development of chips for "Affectionate Intelligence".
Kirsty
Company Specialist at Welcome to the Jungle
Insights
52% employee growth in 12 months
Company
Funding (last 2 of 7 rounds)
Dec 2024
$693m
SERIES D
Jun 2024
$300m
LATE VC
Total funding: $1.3bn
Company HQ
Flemingdon Park, Toronto, ON
Leadership
Jim Keller
(CEO)Previously Senior VP of Silicon Engineering at Intel Corporation, VP of Autopilot Hardware Engineering at Tesla, Corporate VP and Chief Architect at AMD, and VP of Engineering at Apple.
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