Machine Learning Engineer

London, England, United Kingdom · Engineering

Description

Diversity statement

Diversity makes for innovative teams. LabGenius is an equal opportunity employer and we do not discriminate based on gender, race, colour, religion or belief, national origin, age, sexual orientation, marital status, disability, or any other protected class.

About us

LabGenius’ mission is to build an AI-driven platform for the evolution of new biological products.

Since the advent of life 3.8 billion years ago, the survival of all species has depended on rapid innovation at the genetic level. At LabGenius, we're harnessing evolution to develop a new generation of biological products in partnership with world-leading multinationals.

LabGenius is a venture-backed startup founded by Dr James Field in 2012. We’ve raised >£4M in seed financing from top deep-tech investors to mature and automate ‘EVA’ - our AI-powered evolution engine.

About the role

We're looking for an ML Engineer to lead the productionisation of our in-house ML systems. As part of our data science team, you'll work with data scientists and software engineers to build a platform to facilitate training and predicting from models as quickly and efficiently as possible. You'll form a crucial bridge between our investigative data science work and our production learning and optimisation platform.

- Own our ML platform, from design to implementation.
- Contribute to the development of our Python back end services.
- Play a key part in shaping the development and direction of our software platform

Requirements

Essential

- You have 2 or more years of professional experience as a software engineer, using Python or an equivalent language.
- You have at least one year of professional experience training and predicting from ML models at scale in a production setting.
- You are interested in biotech and have a desire to learn more.
- You are excited and motivated by challenging and multifaceted problems.
- You are an excellent oral and written communicator and are able to convey your ideas clearly to a multi-disciplinary team.

Nice to have

- You have experience running and deploying services using Google Cloud Platform tools, particularly CloudML.
- You have experience training and optimising ML models using Tensorflow.
- You have experience using data processing frameworks such as Spark and Hadoop.

Benefits

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