Labviva connects researchers with suppliers of reagents, chemicals, and instrumentation in an intuitive user-friendly platform that supports the priorities of scientists while staying compliant with purchasing rules.
Nicholas Rioux, Co-Founder & CTO, shares everything you need to know about working on the Engineering team at Labviva.
During this video interview, Nicholas discusses:
- Who is Labviva?
- Details on the Engineering Teams
- Interesting Projects & Unique Challenges
- The tech stack
- The interview process
- Why someone should be excited to join the team
Video Sumnmary
Hello, I’m Nicholas Rioux, the CTO and co-founder of Labviva.
Labviva is a technology company dedicated to improving efficiency for life science organizations, including pharmaceutical, biotech, research, and higher education. We provide solutions to help them discover and procure the products they need at appropriate price points from preferred suppliers. In addition, we offer cheminformatics, bioinformatics, and inventory management solutions. Our ultimate goal is to drive efficiencies across how these organizations procure, discover, and utilize their products.
The Labviva Team and Focus
My team encompasses both product and engineering, which together make up about half of the company. We collaborate closely to deliver solutions our customers love. The team is broken up into individual product lines:
- A team focused on our marketplace purchasing platform functionality.
- A team dedicated to our inventory management solution.
- A team focused on data science excellence—harvesting and cleaning data to provide the best information to scientists.
We’ve also invested heavily in developer efficiency, adopting best practices in DevOps and CI/CD. This is structured as a shared service that consults with the other product-oriented teams.
Our work addresses the complex world of managing supplies in a research environment, from small chemicals to large pieces of equipment. We design and implement solutions that allow organizations to discover, procure, and optimize the utilization of all these items. We use advanced data science, AI, and other methodologies to solve mundane problems that often waste scientists’ time. By applying advanced techniques to clean up data, we ensure scientists have the best information when making critical decisions. Essentially, we are at the apex of applying advanced technology to solve common, daily problems, driving major efficiency for our clients.
Technology and Interview Process
Our Tech Stack
Our tech stack is mainly JavaScript-oriented. We utilize a microservices framework laced together with a Kafka implementation, and a heavy investment in React for the front end.
For our infrastructure, we leverage Infrastructure as Code (IaC), using Terraform and Confluent to set up and optimize our environment through extensive CI/CD automation.
Our AI technologies are primarily powered through Snowflake, Snow Park ML, and Python. This modern tech stack is built to scale with our future needs.
The Interview Process
Our interview process focuses on finding people who are professionally and productively additive to our team dynamic and mission. The process involves multiple steps:
- Peer interviews.
- A technical interview focused on solving architectural problems and systems design with teammates.
- Additional interviews with internal stakeholders like Product.
We use this thorough process to ensure every new hire brings the right skill set and perspective to help us solve the difficult problems our customers face effectively.
Impact and Opportunity
We are doing incredibly exciting work with a huge impact. Our customers report over 20% hard savings on their purchases, representing hundreds of millions of dollars in spend.
The most exciting part is that we are directly supporting scientists at the forefront of discoveries that will improve longevity and quality of life globally. We help them achieve this with greater efficiency, cost savings, and productivity.
For engineers, Labviva is highly technology-forward. You get to work on diverse challenges, including:
- Device integrations and scientific applications.
- Complex bio- and chemical informatics platforms.
- Applying AI where it truly adds value across a wide range of interesting use cases and forward-looking challenges.
