המקום בו המומחים והחברות הטובות ביותר נפגשים
We ensure AI, ML and data science are applied in a meaningful way to add customer value, best achieve business objectives and deliver ROI. Unnecessary complexity is avoided and we adopt a creative, fast-fail, highly iterative approach to accelerate ideas from proof-of-concept to go or no-go. Our current capabilities are built on 20+ years of threat analysis and open-source communities with, 40 AI patents granted and 20+ pending. However, we’re just getting started!
About the Role
An AI Engineer II in Model R&D designs and implements AI models that are appropriate for the task at hand, using conventional machine learning, deep learning neural networks and other data science techniques, depending on where their skills lie.
This is greenfield work and speed to market is key - you’ll work fast and smart, supported by more senior members of the team, getting results that let us make decisions about what to start, what to stop, and what to look into more closely. You will have the freedom to take risks and be wrong; even if experiments don’t work out, we iterate and try again. Model R&D happens both locally and in the cloud using IDEs and Jupyter, GPU compute as needed, and deployment to production using cloud services like AWS.
You will:
Research and develop AI/ML/data science models with a focus on delivering customer value.
With guidance and direction, you are able to explore, research and tackle problems while building up your knowledge.
Take responsibility for the quality of your own work and help impact the work of others too.
Be a first-class stakeholder in all parts of the AI R&D process.
Create productive cross-functional relations with teams like engineering, UX, customer research and product management.
Provide informal guidance to new team members.
What we’re looking for technically:
Good conceptual knowledge of a particular field of AI/ML/data science with applied experience. It’s a broad area; we do not expect you to be across it all.
More specifically, building models using Python and any libraries like:
scikit-learn/sklearn; for more conventional ML and data science.
PyTorch/Tensorflow/Keras; if your background is deep learning and neural networks.
Huggingface/Transformers/LangChain; we use these for LLMs.
Pandas/NumPy; for pre-processing and massaging data.
As a bonus, a PhD or MSc using AI/ML/data science. We’re completely open-minded if you’ve got a Software Engineering or related degree and changed lanes into AI.
Any cybersecurity expertise is also a bonus - or maybe your experience comes from capital markets, online marketplaces, healthcare, social media, insurance, or somewhere else.
And you as a person:
Bring a positive, can-do, solution-oriented mindset, welcoming the challenge of tackling new problems.
Are persistent and consistent.
Enjoy working in a fast-paced environment.
Understand the highly iterative nature of AI development and the need for rigour.
Appreciate the importance of thorough testing and evaluation to avoid silent failures in model development.
Are a great teammate to help peers become stronger problem solvers, communicators, and collaborators.
Have a curiosity and passion for continuous learning and self-development.
Stay open-minded, listening to new ideas and suggestions from colleagues, carefully considering and sometimes adopting them.
Realise the importance of wider ethical and risk considerations with AI.
Possess good interpersonal and communication abilities, explaining hard-to-understand topics to different audiences and writing up work clearly.
Exhibit a bias for action, without being careless.
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