Artificial Intelligence and Machine Learning Engineer
3 months ago
About Agnostiq
Agnostiq is the company behind Covalent, a multi-cloud compute framework for scaling GPU workloads from your laptop to the cloud, entirely in Python and without managing any of the infrastructure - enabling simple on-demand access to NVIDIA H100s, A100s & more.
Your Role
As a Machine Learning Engineer specializing in Large Language Models (LLMs) at Agnostiq, you will play a pivotal role in developing and enhancing our AI-driven solutions. With a strong emphasis on LLMs, you will focus on the training and tuning of these models to achieve unprecedented levels of performance and efficiency. This position requires a deep understanding of machine learning principles, particularly in the context of natural language processing and generation. Due to our startup nature, the candidate must be comfortable initiating and taking on new projects independently and with limited supervision.
Key Responsibilities
- Research and implement cutting-edge techniques to improve the efficiency, accuracy, and scalability of LLMs using Covalent Cloud and leveraging high-performance computing.
- Continuously evaluate and integrate the latest advancements in machine learning and LLM research to maintain Agnostiq's technological edge in thought leadership when it comes to leveraging HPC.
- Lead the development and optimization of open-source and proprietary Large Language Models, focusing on innovative training and fine-tuning strategies.
- Contribute to the creation of technical documentation, including technical papers and conference material, blog posts, model architectures, training procedures, and performance analyses, to foster knowledge sharing within the team and the broader AI community.
Required Technical & Professional Expertise
- PhD in Computer Science, Artificial Intelligence, Machine Learning, or a related field, with a focus on Large Language Models.
- Demonstrated experience in developing, training, and fine-tuning LLMs, with a solid understanding of underlying algorithms and architectures.
- Proficiency in programming languages and frameworks commonly used in machine learning, such as Python, TensorFlow, and PyTorch.
- Strong analytical and problem-solving skills, with the ability to work on complex problems and drive projects to completion.
- Excellent communication skills, capable of collaborating effectively with cross-functional teams and presenting complex technical details clearly and concisely.
Nice-to-haves
- Experience with quantum machine learning or an interest in exploring the intersection of quantum computing and LLMs.
- Contributions to open-source projects or published research in relevant areas of machine learning, natural language processing, or artificial intelligence.
- Familiarity with cloud computing environments and tools that support machine learning workflows, such as AWS SageMaker, Google Cloud AI Platform, or Azure Machine Learning.
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