Research Engineer, Tenstorrent

Large Language Model (LLM) Acceleration

Salary not provided

Python

Tensorflow

PyTorch

Senior and Expert level

Boston

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

Salary not provided

Python

Tensorflow

PyTorch

Senior and Expert level

Boston

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

Deep understanding of Transformer based model architecture (e.g., BERT, GPT, LLaMA, FALCON) - should be able to analyse performance bottlenecks in training or inference
Expertise (through evidence in prior projects or research) in large-scale foundation model (CNN/ Transformer/Generative) training and optimisations including quantisation, pruning, low-rank approximation (e.g., LoRA, SVD), sparsity aware training, early exit models, conditional computing, sparse attentions, NAS, fine-tuning (e.g., RLHF, PEFT)
Excellent working knowledge of Python, PyTorch or TensorFlow, (optional C/C++ if interested in compiler and software stack)
Familiarity with and passion for any of the following -- ML compilers, high-performance and massively parallel systems, distributed model training and inference, GPU or AI accelerator architecture -- is a plus

What the job involves

Develop novel LLMs and primitives that take advantage of Tenstorrent’s breakthrough accelerator architecture to deliver orders of magnitude performance & efficiency improvements
Model benchmarking, Performance Analysis, Characterization on novel hardware accelerator
Fine tune/Retrain emerging LLM models after architectural modification to improve performance
Implement ideas from latest research papers and validate new optimizations on novel hardware, suggest improvements on the existing ideas and validate new ideas through experiments
Implement ML models on Tenstorrent’s PyBUDA Compiler framework and analyze results, suggest novel architectural changes to make them more efficient

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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