About Us

We are a VC-backed Scottish AI firm that works with enterprises to build custom AI solutions at scale. We enable enterprises to solve the highest-value problems by distilling smaller, more focused SLMs (Small Language Models).

Our vision is to unlock the value of AI within enterprise environments by addressing complex problems that general AI cannot solve. Malted AI was born out of distillation techniques that helped founders win the 2022 Amazon Alexa Prize where they beat more than 100 teams from around the globe. Since then, the team has raised £7m from leading Venture Capital firms and is helping enterprises deploy AI at scale.

Overview

As a Machine Learning Engineer at Malted AI, you will be instrumental in transforming the vast potential of machine learning into tangible real-world applications, helping enterprises solve complex and high-value problems.

You will be a valuable member of an engineering team designing, developing, and implementing machine learning models and algorithms using data distillation, challenging the frontiers of Artificial Intelligence.

You will work closely with cross-functional teams to understand business requirements, collect and pre-process data, and build scalable solutions

Responsibilities

• Design, build, and maintain machine learning models with an emphasis on LLMs, knowledge distillation, and scalability.
• Create hypotheses, design experiments, and collect results for machine learning model improvement.
• Collaborate with other brilliant minds to push the boundaries of machine learning creating Small Language Models and using knowledge distillation
• Implement best practices and design to ensure high-quality software development including coding, code reviews, testing, and maintenance.
• Engage in continuous learning to keep abreast of the latest machine learning.
• Frameworks and methodologies for domain-specific applications.
• Conduct research and experimentation to explore new techniques, algorithms, and technologies that can drive innovation and competitive advantage.
• Collect, clean, pre-process, and analyse large datasets to extract meaningful insights and features for model training and evaluation.
• Document methodologies, processes, and findings. Communicate results and insights effectively to technical and non-technical stakeholders
• Collaborate with cross-functional teams to integrate ML models into cloud-based infrastructure and full-stack development projects.

Qualifications