We are looking for a motivated and high-achieving Machine Learning Engineer based in London to join our Product team in building a platform to optimise every business on the planet. This is a full-time placement with significant opportunities for personal development.
We offer an intellectually stimulating environment, work within an interdisciplinary team, and an inclusive culture. We are a high-calibre, mission-driven team building a technology that improves our world.
Roles and Responsibilities
We are looking for exceptional and ambitious individuals to develop our Causal AI platform. You will work as a machine learning engineer in the Product team which is composed of software engineers and scientists. A successful candidate will also be able to showcase broader data science and software engineering skills.
Your focus will be on feature engineering, machine learning, and building causal algorithms for time series using Python, Cython, Numpy, Torch, etc.
The broader application stack includes Python, Cython, Numpy, Torch, Django, Celery, Postgres, Redis, Ansible, AWS, GCP, React and other technologies.
This role is open for candidates of all seniority levels, junior to senior.
Current machine learning approaches have severe limitations when applied to real-world business problems and fail to unlock the true potential of AI for the enterprise. causaLens is pioneering Causal AI, a new category of intelligent machines that understand cause and effect - a major step towards true artificial intelligence. Our enterprise platform goes beyond predictions and provides causal insights and suggested actions that directly improve business outcomes for leading businesses in asset management, banking, insurance, logistics, retail, utilities, energy, telecommunications and many others.
We are committed to addressing the diversity problem in the tech industry, and that starts with making sure we have a team where everyone feels at home and can contribute as a peer.
causaLens in the News
Our interview process consists of a coding test, 2 screening interviews and a "Day 0" which is spent with the team. Normally the Day 0 takes place on-site but for the time being they will take place online.
We will do our best to transparently communicate the process with the successful candidates.