Machine Learning Engineer

About Builder.ai 

We’re on a mission to make app building so easy everyone can do it – regardless of their background, tech knowledge or budget.  We’ve already helped thousands of entrepreneurs, small businesses and even global brands, like the BBC, Makro and Pepsi achieve their software goals and we’ve only just started. 

Builder.ai was voted as one of 2023’s ‘Most Innovative Companies in AI’ by Fast Company, and won Europas 2022 ‘Scaleup of the Year’. Our team has grown to over 800 people across the world and our recent announcement of $250m Series D funding (and partnership with Microsoft) means there’s never been a more exciting time to become a Builder.

Life at Builder.ai

At Builder.ai we encourage you to experiment! Each role at Builder has unlimited opportunities to learn, progress and challenge the status quo. We want you to help us become even better at supporting our customers and take AI app building to new heights.

Our global team is diverse, collaborative and exceptionally talented. We hire people for their differences but all unite with our shared belief in Builder’s HEARTT values: (Heart, Entrepreneurship, Accountability, Respect, Trust and Transparency) and a let’s-get-stuff-done attitude.

In return for your skills and commitment, we offer a range of great perks, from hybrid working and a discretionary variable pay or commision scheme, to employee stock options, generous paid leave, and trips abroad #WhatWillYouBuild

About the role

• We’re looking for a curious, ambitious and a razor-sharp engineer to be based in London, UK. You are someone who is passionate about technology and is keen to build machine learning data pipelines that will ingest data and productionize machine learning models in the cloud. You are someone who has an understanding of machine learning and artificial intelligence technologies. You are motivated to drive significant business impact by applying your knowledge and skills. You are able to inspire your colleagues and champion your skills through influence and effective communication. We embrace those who see things differently, aren’t afraid to experiment, and who have a healthy disregard for constraints.
• You will be a part of the AI organization and will work closely and collaborate with global product and engineering teams across many locations including London, New Delhi, Los Angeles, France and Dubai. The Intelligent Systems team will drive all of the innovation powered by data science, machine learning and AI (decision making). It is likely to witness significant growth over the course of the next year and beyond.

Why you should join

• This is a challenging and diverse role that will require you to be a part of the growth of the AI organization from the ground up. The problems we face are unique, requiring us to innovate across a range of stages through invention and research of new techniques, to intelligent implementation and system integration.  Furthermore, this is an opportunity to help grow our suite of products that in conjunction are aiming to automate the entire software development life cycle.

You’ll be responsible for

• Build, maintain and manage data pipelines that support the modeling initiatives of data scientists
• Work closely with data scientists and engineering teams to productionize machine learning models
• Contribute to the development of the Builder Knowledge Graph
• Build unique solutions which ensure that GenAI LLM models in our pipeline have the data they need (through efficient RAG, prompting, agent architectures) and are able to integrate with automated and human workflows efficiently to deliver to our customers and capture feedback for continuous learning.
• Engineer to scale in the cloud using methodologies such as service-oriented architectures, containerized applications and lambdas

• Higher university degree (MSc or PhD) in Computer Science, Engineering, Mathematics, Physics etc
• Solid programming experience in one or more general purpose programming languages: Python (required), Ruby (desirable).
• Software engineering experience applied to productionising machine learning or building data pipelines
• Solid fundamental knowledge of data querying and manipulation using SQL (required), Neo4j (desirable)
• Practical ML experience in R&D, commercial or academic projects
• Ability to communicate with diverse stakeholders

Added bonus:

• Understanding of or experience of building knowledge graphs and graph databases
• Experience working with events type data
• Experience with technologies such as Docker or Kubernetes
• Engineering at scale using production level architectures