Software Engineer - AI & Machine Learning

Dataiku Misc Postings
London
1 year ago
Applications closed

Related Jobs

View all jobs

Software Engineer (AI & Machine Learning)

Software Engineer (AI & Machine Learning)

Software Engineer III - MLOps

Software Engineer, Applied Artificial Intelligence (AI)

Software Engineer, Applied Artificial Intelligence (AI)

Software Engineering Manager – Machine Learning

At Dataiku, we're not just adapting to the AI revolution, we're leading it. Since our beginning in Paris in 2013, we've been pioneering the future of AI with a platform that makes data actionable and accessible. With over 1,000 teammates across 25 countries and backed by a renowned set of investors, we're the architects of Everyday AI, enabling data experts and domain experts to work together to build AI into their daily operations, from advanced analytics to Generative AI. Why Engineering at Dataiku? Dataiku’s on-premise, cloud or SaaS-deployed platform connects many data science technologies, and our technology stack reflects our commitment to quality and innovation. We integrate the best of data and AI tech, selecting tools that truly enhance our product. From the latest LLMs to our dedication to open source communities, you'll work with a dynamic range of technologies and contribute to the collective knowledge of global tech innovators. You can find out even more about working in Engineering at Dataiku by taking a look here. This position is either onsite from Paris, London, Berlin and Amsterdam or remote from these countries. How you’ll make an impact (Note: this is a Software Engineer position, focused on developing Dataiku's features and capabilities. We have separate positions open for Data Scientist, Research Scientist or ML Engineer) Your role will be to add new features or improve existing ones to help our customers use predictive algorithms and put them into production with an intuitive and user-friendly interface, as well as APIs that let coders control and automate all those operations. You may also develop some other AI features (think NLP, time series, computer vision, labeling management, etc.) or collaborate with the AI Lab, our research department, to develop cutting edge POCs and experiments on a broad set of emerging machine learning topics. The platform itself is in Java, Python and Scala for the backend, with JavaScript and Angular on the frontend. Some expected outcomes for this role: Turn ideas or product specifications into full-fledged product features, including unit and end-to-end tests. Tackle complex problems that range from performance and scalability to usability, so that complicated machinery look straightforward and simple to use for our users. Help your teammates: review code, spread your technical expertise, improve our tool chain Bring your energy to the team What you'll need to be successful: You have a significant experience in software engineering You are interested in machine learning and are not afraid of statistics You know that boosted trees are not only about silviculture You can mentor less tenured developers, helping them grow both their technical and non-technical skill You are customer-oriented — you want to understand how the product is used and solve actual customer problems What does the hiring process look like? Initial call with a member of our Technical Recruiting team Video call with an Engineering Team Lead Technical Assessment to show your skills (Home Test or Live Coding) Debrief of your Tech Assessment with Engineering Team member Final Interview with a VP Engineering LI-Remote What are you waiting for At Dataiku, you'll be part of a journey to shape the ever-evolving world of AI. We're not just building a product; we're crafting the future of AI. If you're ready to make a significant impact in a company that values innovation, collaboration, and your personal growth, we can't wait to welcome you to Dataiku And if you’d like to learn even more about working here, you can visit our Dataiku LinkedIn page . Our practices are rooted in the idea that everyone should be treated with dignity, decency and fairness. Dataiku also believes that a diverse identity is a source of strength and allows us to optimize across the many dimensions that are needed for our success. Therefore, we are proud to be an equal opportunity employer. All employment practices are based on business needs, without regard to race, ethnicity, gender identity or expression, sexual orientation, religion, age, neurodiversity, disability status, citizenship, veteran status or any other aspect which makes an individual unique or protected by laws and regulations in the locations where we operate. This applies to all policies and procedures related to recruitment and hiring, compensation, benefits, performance, promotion and termination and all other conditions and terms of employment. If you need assistance or an accommodation, please contact us at: reasonable-accommodationsdataiku.com

Subscribe to Future Tech Insights for the latest jobs & insights, direct to your inbox.

By subscribing, you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

How Many AI Tools Do You Need to Know to Get an AI Job?

If you are job hunting in AI right now it can feel like you are drowning in tools. Every week there is a new framework, a new “must-learn” platform or a new productivity app that everyone on LinkedIn seems to be using. The result is predictable: job seekers panic-learn a long list of tools without actually getting better at delivering outcomes. Here is the truth most hiring managers will quietly agree with. They do not hire you because you know 27 tools. They hire you because you can solve a problem, communicate trade-offs, ship something reliable and improve it with feedback. Tools matter, but only in service of outcomes. So how many AI tools do you actually need to know? For most AI job seekers: fewer than you think. You need a tight core toolkit plus a role-specific layer. Everything else is optional. This guide breaks it down clearly, gives you a simple framework to choose what to learn and shows you how to present your toolset on your CV, portfolio and interviews.

What Hiring Managers Look for First in AI Job Applications (UK Guide)

Hiring managers do not start by reading your CV line-by-line. They scan for signals. In AI roles especially, they are looking for proof that you can ship, learn fast, communicate clearly & work safely with data and systems. The best applications make those signals obvious in the first 10–20 seconds. This guide breaks down what hiring managers typically look for first in AI applications in the UK market, how to present it on your CV, LinkedIn & portfolio, and the most common reasons strong candidates get overlooked. Use it as a checklist to tighten your application before you click apply.

The Skills Gap in AI Jobs: What Universities Aren’t Teaching

Artificial intelligence is no longer a future concept. It is already reshaping how businesses operate, how decisions are made, and how entire industries compete. From finance and healthcare to retail, manufacturing, defence, and climate science, AI is embedded in critical systems across the UK economy. Yet despite unprecedented demand for AI talent, employers continue to report severe recruitment challenges. Vacancies remain open for months. Salaries rise year on year. Candidates with impressive academic credentials often fail technical interviews. At the heart of this disconnect lies a growing and uncomfortable truth: Universities are not fully preparing graduates for real-world AI jobs. This article explores the AI skills gap in depth—what is missing from many university programmes, why the gap persists, what employers actually want, and how jobseekers can bridge the divide to build a successful career in artificial intelligence.