Machine Learning Engineer

Qualis Flow
London, UK
21 May 2019
24 May 2019
Contract Type
Full Time
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 primed for disruption.

Our technology is already being used to improve environmental performance on one of London's largest urban development sites, and our sales pipeline is actively targeting major infrastructure/residential 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

Here at Qflow 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 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 alongside our engineering and product teams, to identify the most efficient ways to structure and process the data we capture. You will own our in-house machine learning capabilities. 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
  • 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
  • Always listening, always learning, but always willing to teach and mentor
  • Always making things as simple as possible, but not simpler
  • 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.
  • Data exploration, analysis and visualization / reporting
  • Building statistical/machine learning models to gain insights and forecast environmental risks from specific construction activities on-site.
  • Experience with relevant research on NLP, adversarial learning, reinforcement learning, active learning, probabilistic bayesian learning, and/or semi-supervised/multitask learning.
  • Proficient at summarising and visualising complex data and pattern finding.
  • Excellent communication skills and an ability to discuss and explain complex ideas.

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

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