Machine Learning Engineer at Senseye
Southampton, United Kingdom
🇬🇧 (Posted Mar 19 2019)
About the company
Senseye is an exciting and rapidly expanding start up in the field of condition monitoring and prognostics. We are developing a cutting-edge cloud product to provide scalable prognostics and advanced condition monitoring to the manufacturing sector. Our team of engineers consist of software engineers, data scientists, UI experts, physicists and mechanical engineers. Following successful investment funding and strong traction from large multi-national clients, we are looking to expand the engineering team.
Do they allow remote work?
Remote work is possible, see the description below for more information.
We are seeking an ambitious and versatile Machine Learning Engineer, whose responsibility will be to understand research generated by our researchers and put this into production software. You will work closely with the wider development and R&D teams to bring research into production.
- Predominantly Golang backend, with Python
- Mongo, Redis, Neo4j and InfluxDB data storage
- Microservice architecture
- Docker + ECS orchestration
- Remote Working available.
- 25 days annual leave + bank holidays
- Start-up vibe – autonomy, trust and excellence
- Dedicated time to give back to the open source community
- Hack Days
Skills & requirements
- BSc/MSc in Computer Science/Engineering (or other numerical discipline). Relevant PhD preferred.
- Experience in machine learning, including supervised and unsupervised techniques
- 2 yrs experience building complex applications with modern best practices (e.g. test-driven development, continuous deployment, code reviews)
- Familiar with the building blocks of scalable cloud systems: Linux, containers and service oriented architecture
- Thrive working on the bleeding edge and can learn new technologies independently
- Comfortable in a culture of fast iteration
- Attracted to complex problems
- Experience working as part of a team on a rapidly growing application and codebase is ideal