AI Software Intern (USA)
Role Overview
This is the team that makes "it runs on Tenstorrent hardware" actually true for real models, at real scale, and builds the infrastructure that keeps the rest of engineering moving fast.
This role is on-site based out of Austin, TX or Santa Clara, CA.
What you might work on: This posting spans multiple teams within Kernels, Models, Inference, Scaleout & our Runtime teams.
One application, one recruiter screen, then we match you to the specific team and location that fits best.
Teams include:
• Kernels: Develops high performance kernels on Tenstorrent hardware
• Models: Optimizes ML models (LLMs, vision models, video and image generation, and other architectures) for our hardware
• Inference Server: development and serving-side optimization
• Runtime: Builds the software engine that manages memory, task scheduling, and code execution on hardware while an application is actively running.
• Scale Out: communication and coordination between devices in a distributed AI system.
• Compiler & Infra: Create tools that optimize AI models into high-performance programs on Tenstorrent hardware, covering memory planning, profiling, debug, and emulation.
Key Skills for This Role
Full Job Posting
Who You Are
- Currently pursuing a BS, MS, or PhD in Comp Sci, Comp Eng, Physics/Math or a related field.
- Coursework or projects any of the following: parallel processing, machine learning or distributed systems.
- A high comfort level as an end user of AI and agentic flows.
- What We Need
- Experience with model quantization, kernel fusion, or other optimization techniques; or low level high performance programming.
- Hands on experience working with Pytorch, experimenting with and deploying models, for inference or training.
- Experience with C/C++ or kernel development in languages like CUDA, or Python and at least one ML framework (PyTorch, TensorFlow, JAX).
- Knowledge of RTL, HDL is nice to have on certain teams.
- What You Will Learn
- How models get adapted to run well on specialized hardware
- How to write high performance code that can get the most out of the underlying hardware
- Production ML serving infrastructure at scale
- How infrastructure choices affect the velocity of an entire engineering org
- USA Hiring Timelines
- This internship opportunity is available throughout our 3 terms with the following corresponding recruitment cycles:
- Winter Term: Jan–Apr work term, Sept–Dec recruit.
- Summer Term: May–Aug work term, Oct–Apr recruit.
- Fall Term: Sept–Dec work term, Jan–Aug recruit.
- Please note these timelines are for reference only. Actual timelines may vary.
- Compensation for all interns at Tenstorrent ranges from $50/hr - $70/hr including base and variable compensation targets. Experience, skills, education, background and location all impact the actual offer made.
- Tenstorrent offers a highly competitive compensation package and benefits, and we are an equal opportunity employer.
- This offer of employment is contingent upon the applicant being eligible to access U.S. export-controlled technology. Due to U.S. export laws, including those codified in the U.S. Export Administration Regulations (EAR), the Company is required to ensure compliance with these laws when transferring technology to nationals of certain countries (such as EAR Country Groups D:1, E1, and E2). These requirements apply to persons located in the U.S. and all countries outside the U.S. As the position offered will have direct and/or indirect access to information, systems, or technologies subject to these laws, the offer may be contingent upon your citizenship/permanent residency status or ability to obtain prior license approval from the U.S. Commerce Department or applicable federal agency. If employment is not possible due to U.S. export laws, any offer of employment will be rescinded.
Preferred Qualifications
- Knowledge of RTL, HDL is nice to have on certain teams.
About Tenstorrent University Jobs
Tenstorrent designs open-source AI processors and RISC-V CPUs, building high-performance computing hardware to accelerate machine learning workloads.
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