Machine Learning Engineer

What you'll do

• Adapt large language models using supervised learning and reinforcement-based methods; plan experiments and refine approaches.
• Build and maintain training-data pipelines and develop scoring methods to assess model accuracy and reliability.
• Partner with researchers, systems engineers, and domain specialists to ship models into production and apply useful research developments.

What they ask for

• Hands-on machine learning experience with hardware design data or electronic design automation (EDA), and prior ML work in EDA in academia or industry.
• Experience taking models end to end, including data gathering, post-training, and accuracy and reliability assessment; creating scoring harnesses and pipelines for large training runs.
• Practical experience customizing pretrained large language models; strong Python and PyTorch skills and familiarity with widely used LLM libraries.
• University degree in an engineering or computing discipline, ideally with a machine learning emphasis; publications at leading EDA or machine learning conferences.

Nice to have

• Experience working with customers to gather requirements and shape technical solutions into products.
• Systems-level inference knowledge, including C++, CUDA, GPU architecture, deployment and optimization; or experience with inference serving, distributed training or inference, low-level profiling, agent workflows, code-generating models, or hardware description languages.

Pay

USD 180,000 to 350,000 per year (base salary)

Tools

Python, PyTorch, C++, CUDA

AI focus

Adapt large language models to specialized hardware-design tasks, create training data, and evaluate model quality and reliability.