LIMS Engineer
ROLE
As the LIMS Engineer you will be a key member of our Research & Development team responsible for building out, integrating, and administering our laboratory informatics systems (in particular our LIMS, ELN, and data management). These systems partner with Hampton Creek’s research scientists and discovery pipeline. At Hampton Creek, our mission is to reinvent the food system and our discovery initiative uses biochemical and functional assays to identify novel plant-based food sources and develop them into delicious, sustainable, affordable foods. The LIMS engineer will work with software engineers, bioinformatics data scientists, biochemists, analytical chemists, and food scientists. You will model assays, create user interfaces to collect and access data, and administer the systems that tie it all together.
ABOUT YOU
We’re looking for a scientist and technologist with a related degree and 4+ years experience working in the life sciences and engineering / IT / informatics.
You…
– Understand lab research workflows and practices
– Have experience with laboratory instruments and assays, ideally biochemical and/or protein characterization
– Consistently deliver functioning, robust information systems
– Have experience with a data processing language, preferably Python or R.
– Have experience integrating with HTTP APIs
– Understand data modeling and are proficient working with relational databases
– Have empathy for your users and a collaborative mindset
– Value individuals, interactions, and working software
– Thrive in a fast-paced startup
RESPONSIBILITIES
- Apply technology to accelerate the work of all research teams
- Coordinate with team leads to identify and refine requirements
- Build, maintain, and extend lab informatics tools and applications
- Work with research scientists to model assays in the Core LIMS
- Work with automation engineering to ensure reliable data acquisition from instruments
- Work with data engineering to integrate analytic pipelines and data stores
- Work with bioinformatics to provide access to experimental data and ensure clean, validated data is available to feed into machine learning models
- Work with sourcing to facilitate material and sample registration
- Automate data cleaning
