Software Development Engineer - Intern

You will be deep involved in the latest cutting-edge AI Cancer Genome Analytic World.  Where AI / Cloud / Peta byte/ Big Data is not only a word. Here it is really happening. You will be working together with a Team, which is building the Structure of the Data Science Department, which handles Petabytes of Data, supporting 1000s Genome Patient Data every day. Helping Patients around the world, to fight the hardest Diseases from Cancer to Rare Diseases. It is real world AI which analyses a cancer patient Genome, to help them with diagnostics to support them on their hardest journey. Your work will change people’s life, no boring thousandth crypto app. We offer you purpose, this work matters and has real life impact. 

  

You will be involved in:

• Big Data Storage Analyse 

The mission of supporting 1000 Genome Analysis per day requires Data, a lot of it, Petabyte Level. These Data has to be handled and stored smart. You have to help to make this even smarter, to find out where we waste money, where we could use the money smarter. To understand and document what structure it has and propose better ideas. 

• AI Computing Structure 

We have to process the data with large cloud computing setups, with latest NVIDIA GPUs. This newest hardware is costly, and we must use it efficient, you need to tell us and document where we can improve efficiency with insights and statistics, to make smarter decisions. We must find a meaningful baselines to make smarter cost decisions. To improve the cost structure and make Genome Cancer Diagnostics more affordable around the world.  

• Cloud Environment 

Get in touch with the latest products Microsoft Azure Cloud has to offer, not only the basics, the latest. Where information is fresh and not a lot of people worked with. Propose and test out innovative ideas to optimize our cloud setup. 

• Review & Optimize Architecture 

You will analyse and document our architecture. Review the Processes of the BioTech AI Data Scientist to identify inefficiencies and improve them. The main objective is to understand the Architecture and propose Cost saving inititives to make AI Genome Analytics available for more people around the world. 

Requirements

• You can code, you don’t have to have the experience of a M. Odersky, but we won’t teach you the basics. 
• You understand “Cloud“ not only from the media, it is not only “other persons computer”. You will be able to skim through the million buzzwords of the cloud world and differentiate between marketing and tech. You already tried out different technologies on Azure, AWS or GCloud, using it for POC or similar. 
• You know how to kick off a project, you can take a problem statement and write from scratch some lines of code to analyse data and display it in a professional way. You did this already a few times in a hackathon/student project/pet project, of course not on Genome Cloud computing, but maybe on game/app idea you had, or a collaboration project at the university. 

This internship is for 6 months or more.

Benefits

You will be joining an organisation with the patient at the heart of every decision and action, driven by purpose as we drive exponential growth.

Business recognition and accolades include:

·      World's most innovative companies (Top 10)

·      World's smartest companies (Top 50)

·      100 Best Places to Work in Boston

·      Top 10 European Tech Startup - 2020

·      Top 10 European biotechs startup to watch - 2021

·      Top 25 East-Coast Biotech to watch - 2021

Plus, you will find:

·      A flexible, friendly, and international working environment with a collaborative atmosphere

·      Flexible working hours, including hybrid remote / on-site working model

·      An exciting company mission that brings together science and technology to directly impact the lives of patients with life threatening illness

·      A fast-growing company with plenty of opportunity for personal growth and development

·      A hard technical challenge to solve with exciting modern technology - cloud computing, Big Data, DevOps, machine learning