Machine Learning Engineer

Lifebit Published: May 16, 2018


Lifebit is building the world’s first intelligent genomics platform that understands DNA data and generates meaningful insights.

We just closed an over $3M funding round from two major London funds after graduating from Techstars London 2017's cohort.

We are building a cloud-based cognitive system that can reason about DNA data like humans do. This offers researchers/R&D professionals, and their corresponding organisations (ie. pharmas), a highly scalable, modular and reproducible system that automates the analysis processes, learns from the data and provides actionable insights.

Our tech team is split into:
1. Engineering and Machine Learning:
• Stack: Node JS, React, MongoDB
• Building Lifebit’s platform.

2. Bioinformatics and Genomics:
• Building Lifebit’s genomics modules: end-to-end solutions for specific operations and analysis.
You will be working with a very talented and cross-functional team with 10 years of experience in science, software engineering and web development. Experience working in startups and small accelerating teams will be a plus.

We are looking for a someone who is interested in working with state-of-the-art Machine Learning and Deep Learning methods, working with complex genetic data. You will design and implement novel approaches to learning from large scale data using ML models and implement visualisation layers to summarise it.

Required experience:
• Background in statistics, machine learning and data science.
• Experience with relevant research on NLP, adversarial learning, reinforcement learning, active learning, probabilistic bayesian learning, and/or semi-supervised/multitask learning.
• Experience in applying statistical approaches while development software solutions for data analysis.
• Experience in genetics/genomics and medical sciences is a plus but definitely not a must.
• Proficient at summarising and visualising complex data and pattern finding.
• Excellent communication skills and an ability to discuss and explain complex ideas.

Jop type:
• Full-time

Job Location:
• London, we don't allow fully remote but are open to some flexibility. If you're based in Cambridge, for example, we'd cover your commute to London and after the initial on-boarding period, we'd be happy to explore being at the office for just 3 days/week.

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