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Machine Learning intern en London


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Descripción de la oferta

Why Qflow?

At Qflow our vision is to transform the way we build our cities, by using real-time data from construction sites to eliminate environmental and social impact.

We are an early-stage start-up constantly looking to push boundaries in a fast-paced environment, so naturally we like a challenge. We believe that all harmful impacts from construction are preventable, and we are looking for talented individuals to join us on a mission to transform an established industry that is primed for disruption.

Our technology is already being used to improve environmental performance on 14 construction sites across the UK including one of London’s largest urban development sites, and our sales pipeline is actively targeting major infrastructure and building construction projects across the UK.

At the heart of our business is a focus on people – building a great team around a culture of trust, collaboration, and creativity. A team that will be supported to grow and develop with the business.

We are VC-backed with support from Entrepreneur First and the Royal Academy of Engineering. We are led by construction and technology professionals with a deep understanding of the tasks ahead.

The Opportunity

We are building an integrated platform capable of gathering and aggregating huge volumes of environmental telemetry in real-time. We are applying machine learning techniques to process this data and understand the causal links between construction works schedules and environmental data, to facilitate better management of key environmental risks throughout a construction programme.

In order to meet this challenge, we are looking for a talented machine learning engineer who is looking for an opportunity to develop themselves as part of a growing company. This is that rare chance to be one of the first employees in a new and exciting venture, help steer the direction of the engineering team, and change the way the very cities in which we live are built and maintained.
You will be working closely with our amazing head of machine learning and alongside our engineering and product teams, to identify the most efficient ways to structure and process the data we capture. This will involve the creation of a library of ML models, specially trained for particular predictive tasks and investigating causality between construction activities and environmental impacts. You will be applying ML techniques to our proprietary data to uncover patterns and understand new ways to predict environmental risk on construction projects.

Candidate Profile

You should thrive in fast-paced environments and employ creative problem solving to overcome challenges, you care about impact and are constantly seeking the necessary conversations to ensure that you build the right system at the right time. You see your job as making that impact, not just completing tasks.

• A genuine engineer at heart who’s an advocate of SOLID, DRY, and best practices (looking beyond the theory)
• Driven to use exciting technologies to solve real-world problems
• Always looking to use the right tools/technologies for the job, aiming to making things as simple as possible, but not simpler
• Always listening and learning, and willing to teach us new tricks!
• Willing to take accountability for the choices (and mistakes) you make
• Able to solve technical and non-technical problems independently, but willing to ask for help when necessary
• Willing to work closely with Engineering and Product members as part of a cross-functional team
• Nurturing a sense of trust and constructive criticism within the team
• Able to recognise the best in yourself and bring out the best in others

Skills and Experience

• Background in statistics, machine learning and data science.
• Solid understanding of machine learning algorithms (e.g., support vector machines, random forest, neural networks).
• Experience with relevant research on Optical Character Recognition (OCR) and Natural Language Processing (NLP).
• Solid understanding of Python and its machine learning frameworks (e.g., scikit-learn, keras).
• Ability to quickly prototype, develop and deploy machine learning models.
• Ability to multitask, prioritize, show initiative, and respond quickly in a fast-paced collaborative environment
• Excellent communication skills and an ability to discuss and explain complex ideas in clear and simple terms.

Tech Stack
C#, .Net Core, ASP.NET Core, EF Core, Azure, Docker, React, Angular, Bootstrap, JQuery, SASS/LESS, REST/GraphQL, Typescript, Machine Learning, Serverless, GitHub, DevOps, Python

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