Software Engineer - London
London, UK /
London, UK – Software Engineering - London /
all.health is at the forefront of revolutionizing healthcare for millions of patients worldwide. Combining more than 20 years of proprietary wearable technology with clinically relevant signals, all.health connects patients and physicians like never before with continuous, data-driven dialogue. This unique position of daily directed guidance stands to redefine primary care, while helping people live happier, healthier and longer.
You will work closely with the data scientists in the London team as well as the engineering team in San Francisco for building software for production or facilitating model construction. You will work closely with data engineers on designing and developing data storage and access infrastructure, data processing pipelines and predictive services. You will be responsible for selecting and integrating any Big Data tools and frameworks required to provide requested capabilities.
Skills and Qualifications
- Proficient in Python
- Experience working with microservice architecture
- Familiar with agile software development techniques
- Experience with continuous integration for software deployment
- Good understanding of run-time efficiency and scalability of services in production
- Solid background in data storage solutions including SQL and NoSQL such as MongoDB
- Good knowledge with cloud services such as Azure, AWS or GCE
- Minimum of 3 years of relevant work experience
Further skills (bonus)
- Experience with Big Data solutions such as Spark,, Hadoop, etc
- Good understanding of ETL processes
- Good understanding of distributed computing principles
all.health has developed a comprehensive preventative and proactive healthcare platform that combines clinical-grade sensors, machine learning, patient histories, insurance claims data, and other information to provide real-time at-risk screening for several disease conditions; these include acute respiratory infections such as COVID-19, and chronic conditions such as hypertension and diabetes. Contextualized 24/7 data along with clinician input and interventions will then be used to guide positive behavior changes. The premise is to catch various health conditions early and help reverse or manage the negative effects.