Senior & Staff Machine Learning Scientist
My client, an AI software firm building a complete, AI-driven customer service platform, is looking to hire exceptional engineers to join their Machine Learning team in Dublin, Ireland.
This team is responsible for defining new ML features, researching appropriate algorithms and technologies, and rapidly getting first prototypes into customers’ hands.
An extremely product-focused team, they work in partnership with Product & Design functions of teams they support. The team’s dedicated ML product engineers enable them to move to production fast, often shipping to beta in weeks after a successful offline test.
They are very passionate about applying machine learning technology, and have productized everything from classic supervised models, to cutting-edge unsupervised clustering algorithms, to novel applications of transformer neural networks. They test and measure the real customer impact of each model deployed.

WHAT SKILLS MIGHT YOU NEED?

These are meant to be indicative, not hard requirements.

• Broad applied machine learning knowledge
• Senior - 5+ years’ applied ML experience
• Staff - 7+ years’ applied ML experience
• Practical stats knowledge (experiment design, dealing with confounding, etc.)
• Intermediate programming skills
• Strong communication skills, both within engineering teams and across disciplines
• Comfort with ambiguity
• Typically have advanced education in ML or related field (e.g. MSc)
• Scientific thinking skills

BONUS SKILLS & ATTRIBUTES

• Track record shipping ML products
• PhD or other experience in a research environment
  Whilst we carefully review all applications, to all jobs, due to the high volume of applications we receive it is not possible to respond to those who have not been successful.

• Identify areas where ML can create value for customers
• Identify the right ML framing of product problems
• Working with teammates and Product & Design stakeholders
• Conduct exploratory data analysis and research
• Deeply understand the problem area
• Research and identify the right algorithms and tools
• Being pragmatic, but innovating right to the cutting edge when needed
• Perform offline evaluation to gather evidence an algorithm will work
• Work with engineers to bring prototypes to production
• Plan, measure & socialize learnings to inform iteration
• Partner deeply with the rest of team, and others, to build excellent ML products