AI/Machine Learning Engineer

AI/Machine Learning Engineer

Moonshot believes that marginalised people in society — including minority ethnic people, people from working class backgrounds, women, disabled and LGBTQIA+ people — must be centred in the work we do. We strongly encourage applications from people with these identities or who are members of other communities who are currently underrepresented in our workforce. We know a diverse workforce will enable us to understand drivers behind violent extremism and online harms in an in-depth way and do better work to counter them.

About the role:

Are you passionate about a career in tech and genuinely interested in working for an organisation tackling online harms, from violent extremism and gender based violence to disinformation and child online exploitation?

We have a rare and exciting opportunity for an AI/Machine Learning Engineer to join Moonshot, supporting the Director of Engineering. We are building our Ireland based development team from the ground up, and we're looking for an experienced individual to help with this effort and take our software to the next phase of its evolution. This role involves regular exposure to violent, extremist, and otherwise harmful content, including material related to online radicalisation and abuse, as part of dataset review, model training, and evaluation work.

You are someone who:

• Is passionate about producing high quality, user focussed software systems, and keeping them that way.
• Understands how to use AI/ML to get insights out of large datasets.
• Loves working in a high performance team and operating environment, built on mutual trust and continuous improvement.
• Wants to grow their technical and non-technical skills and responsibilities in a unique organisation.
• Deeply cares about using their skills to solve complex problems, using technology to disrupt online harm and protect vulnerable people.

Your responsibilities will include:

• Developing, training, tuning and running ML models on our data to get answers to key questions.
• Integrating and maintaining these models within our software products.
• Monitoring model inference outputs to assess performance degradation over time.
• Establishing feedback mechanisms with end users to continuously improve model performance.
• Developing and integrating new analytical algorithms to run against large datasets.
• Guiding our teams on how best to utilise AI/ML in ethical, appropriate and values driven ways.