Machine Learning Engineer
As a Machine Learning Engineer at Util you will tackle a wide range of problems, from designing efficient web-scraping algorithms, to building natural language processing and statistical inference models. Your work will involve real-world, dirty and diverse datasets and a crucial part of your job will be efficiently collecting and cleaning the data in addition to building analytical models. While you will own your specific set of problems to be solved, expect to work closely with the rest of the engineering team to ensure the different solutions integrate with one another efficiently.
As one of the first members of our growing team, we expect you to contribute to all aspects of Util, including strategy, company culture and product development.
What You'll Do*:
Collecting and cleaning large datasets;
Build state-of-the-art methods for extracting knowledge from vast amounts of textual data;
Research, prototype and build statistical inference models;
Write production ready code;
Write tests to ensure reliability of your code;
* These tasks will be distributed across our product development team, with a variety of skillsets, including backend engineering, data science and machine learning/natural language processing and consequently we do not expect any one candidate to be an expert in every focus area.
What We Look For:
Core programming expertise in one of the following languages: Python, MATLAB, or R;
Experience with Machine Learning models, ideally Natural Language Processing;
Strong knowledge of statistics and experience identifying and applying relevant models;
Familiarity with some of the following libraries: SciPy, Pandas, NumPy, TensorFlow, Keras, SpaCy, Gensim;
Some working knowledge of databases;
Experience with version control (git);
Ability to communicate complex technical information to a non-technical audience ;
Ability to work individually and in a team;
Flexibility and ability to adapt to changes in priority, with the understanding that process and structures are not rigid at an early stage startup;
An iterative problem-solving approach, with a “get it done and then improve” not “get it perfect” mentality;
Comfort with uncertainty - where others see unchartered territory you see the opportunity to innovate;
The desire to work in an idea meritocracy, where all ideas are welcomed and the only metric is quality.
Generous option package;
Half a day a week dedicated to learning something new;
A staff free book policy; purchase one free book a week and contribute to our growing library;
Flexible working as standard;
Central London location;
3-month probation period;
Start-date: 3rd September (or earlier if possible).