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causaLens are the pioneers of Causal AI — a giant leap in machine intelligence. We build Causal AI-powered products that are trusted by leading organisations across a wide range of industries. Our Causal AI Platform empowers all types of users to make superior decisions through intuitive user interfaces and APIs that adapt to their level of technical expertise. We are creating a world in which humans can trust machines with the greatest challenges in the economy, society, and healthcare. We are looking for motivated and high-achieving Senior Fullstack Software Engineers focusing on bringing causality, explainability and accountability to MLOps as a first on-the-ground engineering member of our CausalOps team, joining product and data scientists. We are a mission-driven, interdisciplinary team with an inclusive culture building technology that improves our world. This is a full-time placement with significant opportunities for growth in a rapidly expanding team. Roles and Responsibilities As a Senior Full Stack Engineer, you will be involved with the product at all stages of development, from initial conception of a feature all the way to being deployed to our customers. You should have strong technical and communication skills and should want to take ownership of what you are building. You will be expected to push forwards your own ideas as well as be comfortable mentoring and coaching junior members of the team. What You’ll Be Working On You will be expected to work across our application stack and be willing to learn any areas which you do not already have experience with. Our primary languages are Python for backend and machine learning code on FastAPI based apps, with Typescript + React for our frontends. Your primary focus will be streamlining the operationalisation of causal machine learning models, the workflows for delivering models, as well as the dashboards and system processes to view and understand how models are performing. Accelerating the workflow of data scientists and ML Engineers. Removing obstacles in their way to delivering real value to their customers. You will be helping us advance the state of the art of MLOps by bringing the strengths of causal AI to bear to build leading indicators of model sensitivity, visibility on model recourse and A/B testing. Our infrastructure runs on Kubernetes, familiarity with cloud services, docker and modern CNCF stacks is desirable. Requirements Candidates would ideally have 4 years professional experience with scalable service based SaaS deployments. Ideally within the Machine Learning lifecycle. Strong Development experience in Python, with experience in services in a SaaS environment. Ideally using FastAPI.Good understanding of front end interfaces and visualisation techniques with REST backends and SPA frontends in React.Solid knowledge on infrastructure automation and cloud native systems, especially based on docker and kubernetesSolid knowledge of ML deployment processes and MLOps best practicesProven capability of maintaining high quality in a fast paced delivery environment with strong knowledge of the software development lifecycle (code review, version control, tooling, testing, etc.)Familiarity with the data science process and machine learning based workflows and feedback loops. Ideally you should be able to work in London or be able to commute. Nice to Haves Knowledge and experience with GoLang.Demonstrable experience in implementing MLOps solutions. Experience with open source MLOps frameworks such as MLFlow or Kubeflow or proprietary platforms such as AWS Sagemaker or Azure MLOpsInterest in scientific data visualisation is a plus and any experience with visualisation libs, such as d3, would be very valuable.A broad understanding of data science and machine learningInterest in data pipelines and processing large (>100GB) datasets The Company 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. causaLens in the News causaLens raises $45m Series A to scale Causal AI - Tech CrunchBest 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 BenefitsWe may be biased but we believe you’ll be in good company. 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! This truly is an exciting opportunity to join a fast-growing and agile team, passionate about innovation and making a difference.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: Share Option SchemeGenerous education and self-development budgetHybrid working - a combination of remote and from the office as required, along with all the tech you need to get the job done (MacBook Pro etc.)Work abroad 2 weeks per annumCycle to work schemeRegular team socials inc. ‘Achievements & Global Cuisine Thursdays’ and annual company retreatOffice snacks and drinks to stay refreshed Logistics Our interview process consists of a few 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.
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