Here at BenchSci, we accelerate drug discovery by using machine learning to facilitate successful experiments. We’re backed by Gradient Ventures, Google’s AI fund, and built by life scientists for life scientists. We’ve become the world leader in AI-assisted antibody selection, and we’re growing!
We are seeking a DevOps Engineer to join our Cloud DevOps Team. BenchSci is rapidly expanding its engineering teams and tackling new problems, and we need to scale our infrastructure. You will build CI/CD pipelines, improve Infrastructure-as-Code, and implement Site Reliability Engineering principles to reduce overhead in provisioning our services.
What you’ll do:
- Make our entire cloud infrastructure a one-button deployment
- Work with engineers to recommend architectures, best practices, and integrations
- Manage server configurations in-cloud and identify and fix any security concerns
- Increase observability and resilience of our core product using SLIs/SLOs/SLAs
- Optimize architecture and make life easier for international stakeholders
- Maintain and build out kubernetes clusters and scalable computing clusters
- Constantly learn, improve, and teach others how to do the same
Who we’re looking for:
- Experience with infrastructure management tools (Deployment Manager, CloudFormation, Terraform, etc.)
- Experience building continuous integration (CI) and continuous delivery (CD) pipelines (CircleCI, Jenkins, TravisCI, etc.) and their integration with Git
- Proficiency with Linux operating systems (Ubuntu, RHEL, etc.) and Bash scripting
- Strong grasp of networking fundamentals (Load balancing, HTTP protocols, REST)
- Experience building cloud-native solutions on Google Cloud Platform (GCP) and Amazon Web Services (AWS)
- Experience with configuration management tools (Ansible, Puppet, Chef, etc.)
- Experience with different approaches to dynamic scaling, such as GKE and Lambda
Bonus points for:
- Experience working with large scale software and production environments/databases
- Experience optimizing high-availability clusters (e.g. Elasticsearch, Neo4j)
- Familiarity with SRE principles, and how to monitor and improve infrastructure
- Familiarity with configuring VPCs and software-defined network security rules
We empower the world’s scientists to run more successful experiments to accelerate drug discovery.
With a 50% failure rate, inappropriate antibodies waste millions of research dollars and delay novel therapies by months. BenchSci’s AI solves this problem by continuously reading biomedical papers like a PhD biologist to understand which antibodies have been successfully used in which experiments. Unlike traditional antibody search tools, BenchSci uses comprehensive data, proprietary machine learning models, sophisticated bioinformatics and ontologies, proprietary image recognition technology, and a unique interface to increase confidence in antibody selection. This reduces the cost of research and increases its impact for over 31,000 scientists at more than 3,600 academic institutions and 15 of the top 20 pharmaceutical companies that trust BenchSci to guide their experiment design.
Here at BenchSci, these are our core values:
Focused: We focus on what will drive the greatest impact at all times.
Advancement: We believe in continuous growth, and discovering new ways to do things better. This applies to our product and business, but also to ourselves.
Speed: We recognize that without a sense of urgency, our team, our product and our mission lose their value.
Tenacity: What we’re trying to do isn’t easy, but we hire the best people, and give them the autonomy, tools, and resources to succeed. The hard work is up to them.
Transparency: We believe that sharing diverse ideas and information creates strong teams. Our success stems from research, collaboration, feedback, and trust.
BenchSci is an equal opportunity employer. We value diversity and are committed to fostering an inclusive environment. All four of our cofounders are immigrants to Canada, as are many of our employees. We welcome your fresh perspectives and ideas.