Software Engineer, Tenstorrent

AI Tools

$100-500k

Salary applicable for US based candidates, may vary in other locations

Python

Tensorflow

C++

CUDA

PyTorch

Junior, Mid and Senior level

Austin

San Francisco Bay Area

Toronto

Next-generation computing company building computers for AI

Job no longer available

Next-generation computing company building computers for AI

1001+ employees

B2BArtificial IntelligenceEnterpriseManufacturingDeep TechMachine Learning

Job no longer available

$100-500k

Salary applicable for US based candidates, may vary in other locations

Python

Tensorflow

C++

CUDA

PyTorch

Junior, Mid and Senior level

Austin

San Francisco Bay Area

Toronto

1001+ employees

B2BArtificial IntelligenceEnterpriseManufacturingDeep TechMachine Learning

Company mission

To bring unique solutions to the problems facing AI and machine learning.

Job

Company

Role

Who you are

If you like blending systems programming with machine learning to solve real performance problems, you’ll feel right at home
Skilled in Python and C++ with a strong grasp of algorithms, data structures, and performance tuning
Comfortable working with machine learning frameworks like PyTorch or TensorFlow and curious about applying LLMs to systems work
Experienced with GPU programming (CUDA, OpenCL, ROCm) and optimizing kernels for speed and efficiency
Excited by the idea of automating tedious development tasks and turning them into intelligent, AI-driven workflows

What the job involves

As a Software Engineer on the AI Tools team at Tenstorrent, you’ll build intelligent systems that help developers create faster, more efficient compute kernels for our AI hardware
You’ll combine low-level performance know-how with AI techniques to automate optimization, debugging, and kernel generation
Design AI-powered tools that help developers write, optimize, and debug compute kernels
Integrate these tools into existing compiler and development workflows
Build automated profiling, testing, and analysis systems that flag performance and correctness issues
Experiment with novel AI and ML approaches for program synthesis, kernel optimization, and automated debugging
What You Will Learn:
How to merge AI techniques with low-level systems programming for real-world performance gains
Ways to leverage large language models for code generation, optimization, and developer tooling
How to work closely with hardware, compiler, and ML teams to bridge high-level AI ideas with low-level execution
Strategies for building scalable, intelligent development infrastructure for advanced semiconductor products

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

Top investors

52% employee growth in 12 months

Glassdoor (4.5)

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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