causaLens is the pioneer of Causal AI — a giant leap in machine intelligence.
We are on a mission to build truly intelligent machines - it’s hard but super fun! If you want to build the future and are looking for a place that values your curiosity and ambition, causaLens is the right place for you. Everything we do is at the forefront of technological advancements and we are always on the lookout for people to join us whose skills and passion tower above the rest.
Since the company was established in 2017, causaLens has:
🥳Launched decisionOS, the first and only enterprise decision making platform powered by Causal AI - here
🦄Raised $45 million in Series A funding
🏆Named a leading provider of Causal AI solutions by Gartner - here
🚀Included in Otta’s 2022 Rocket List as one of the fastest-growing companies to launch your career
To radically advance human decision-making.
A world in which humans leverage trustworthy AI to solve the greatest challenges in the economy, society and healthcare.
Head to our website homepage and watch the ‘Why Causal AI’ video to learn more.
Join causaLens as a Machine Learning Engineer specializing in Causal AI and make a significant impact in advancing our Causal AI platform. Collaborate with a talented team, leverage cutting-edge technologies, and be part of the forefront of research and development in Causal AI. This role offers the opportunity to grow professionally, regardless of your seniority level, and contribute to groundbreaking solutions that push the boundaries of machine learning and causality. We are looking for a motivated and high-achieving individual.
This is a full-time placement based in London.
What You Will Do
- Collaborate closely with software engineers and scientists to enhance and expand our Causal AI platform.
- You will contribute to feature engineering and the development of machine learning models with a focus on causality.
- Utilizing Python, Cython, Numpy, Torch, and other relevant technologies, you will build robust causal algorithms for time series and/or tabular data.
- Collaborate with cross-functional teams to ensure seamless integration of the Causal AI platform within our infrastructure.
- You will participate in code reviews, provide feedback, and help maintain code quality.
Stay updated with the latest advancements in Causal AI, actively sharing knowledge and driving innovation within the team. Contribute to documentation efforts, including research findings, methodologies, and technical specifications. Embrace a continuous learning mindset, seeking opportunities to expand your data science and software engineering skills.
- Bachelor's or master's degree in Computer Science, Engineering, Mathematics, or a related field with a solid understanding of statistical concepts and machine learning principles. Familiarity with causal inference or Ph.D. is a plus.
- Experience in developing and implementing machine learning algorithms and models and familiarity with machine learning libraries and frameworks (e.g.,TensorFlow, PyTorch, scikit-learn).
- Proficiency in Python, with the ability to translate advanced machine learning algorithms into code.
- An in-depth understanding of computer architecture, including knowledge of languages such as C, C++, or Cython, is preferable.
- Familiarity with software development best practices, version control systems, and agile methodologies is desired.
- Highly capable, self-motivated, collaborative, and personable, with a drive for integrity and excellence.
- Natural curiosity, creativity, and effective problem-solving skills, with a passion for tackling cutting-edge challenges.
- Excellent written and verbal communication skills, with a high level of business acumen.
- Ability to work independently and thrive in a fast-moving environment.
- Based in London or willing to relocate.
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 may be biased but we believe you’ll be in good company. We offer a hybrid working set up and are dedicated to building an inclusive culture where diverse people and perspectives are welcomed. Aside from joining a smart and inspiring team, you’ll be amongst people who are always there to support your ideas and encourage you to grow. We celebrate our differences and come together to share our triumphs!
causaLens in the news
- causaLens raises $45m Series A to scale Causal AI - Tech Crunch
- Best Deeptech Company 2019 - Artificial Intelligence Awards
- ‘Meet causaLens, a Predictive AI For Hedge Funds, Banks, Tech Companies’ – Yahoo Finance
- ‘The U.K.’s Most Exciting AI Startups Race To Scale’ - Forbes
- ‘AllianzGI Taps Virtual Data Scientists amid War for Talent’ - Financial Times
- ‘Machine Learning Companies to watch in Europe’ - Forbes
- 'causaLens Appoints Hedge Fund Veteran and Data Leaders to Advisory Board’ - Newswire
- ‘Best Investment in Deeptech’ award - UK Business Angels Association Awards
- ‘100 Most Disruptive UK Companies’ - Hotwire
What We Offer
We care about our people’s lives both inside and outside of causaLens. Beyond the core benefits like competitive remuneration, pension scheme, paid holiday and a good work-life balance, we offer the following:
- Access to mental health support through Spill
- Competitive salary
- Annual Discretionary Bonus
- 25 days paid holiday plus bank holidays
- Share options
- Pension scheme
- Happy hours and team outings
- Referral bonus program
- Cycle to work scheme
- Friendly tech purchases
- Office snacks and drinks
Our interview process consists of a couple screening interviews and a "Day 0" which is spent with the team (either in the office or virtually, whatever you feel comfortable with). We will always be as transparent as possible so please don’t hesitate to reach out if you have any questions.