TetraScience

DevOps Engineer- Boston

Boston, Massachusetts, United States

Not SpecifiedCompensation
Mid-level (3 to 4 years), Senior (5 to 8 years)Experience Level
Full TimeJob Type
UnknownVisa
Scientific Data, AI Cloud, Life Sciences, BiotechnologyIndustries

Requirements

Candidates should possess a Bachelor's degree in Computer Science, Engineering, or a related field, or equivalent practical experience, with at least 5 years of professional experience as a DevOps Engineer or in a similar role. Extensive experience with Amazon Web Services (AWS), including AWS certification, is required, along with proficiency in at least one programming language like Python, Java, or Go. Strong proficiency in infrastructure-as-code (IaC) tools, preferably CloudFormation, and good understanding and practical experience using and deploying AI (ML, LLMs) are essential. Expertise in automating cloud-native technologies, deploying applications, and provisioning infrastructure, along with experience with cloud-native CI/CD workflows and tools such as Github Actions or AWS CodeDeploy, is necessary. Familiarity with microservices architecture, containerization, and orchestration tools like Docker and Kubernetes, a solid understanding of Linux basics, networking fundamentals, and security best practices, and excellent communication skills are also required.

Responsibilities

The DevOps Engineer will collaborate with product and engineering teams to drive and enhance the entire lifecycle of products, from design and development to deployment and operation. They will work closely with clients to deploy and troubleshoot products in clients' AWS environments, ensuring smooth integration and optimal performance. Responsibilities include developing CloudFormation templates, Terraform modules, Python scripts, deployment frameworks, monitors, and self-healing tools to automate processes and improve efficiency. The engineer will assist the software engineering team in building accurate monitoring and metrics systems for applications before they go into production. They will manage internal AWS environments and network, ensuring stability, security, and scalability while controlling costs. Additionally, the role involves participating in meetings with potential clients, working alongside solution architects to address their questions and concerns regarding product integration into client networks and AWS accounts, and maintaining up-to-date documentation on deployments, processes, and standard operating procedures.

Skills

AWS
CloudFormation
Terraform
Python
Monitoring
CI/CD
Deployment
Troubleshooting
Data Management

TetraScience

Cloud platform for scientific data management

About TetraScience

TetraScience offers a cloud-based platform called the Scientific Data Cloud, which helps biopharmaceutical companies manage and harmonize their scientific data for research and development, quality assurance, and manufacturing. The platform connects various lab instruments and software, streamlining data management and significantly reducing task completion time. TetraScience's vendor-neutral and open design allows it to work with any lab equipment, making it a flexible solution in the life sciences sector. The company's goal is to enhance scientific outcomes by preparing data for artificial intelligence and machine learning applications.

Boston, MassachusettsHeadquarters
2019Year Founded
$113.8MTotal Funding
SERIES_BCompany Stage
AI & Machine Learning, Biotechnology, HealthcareIndustries
51-200Employees

Benefits

Unlimited PTO
100% company paid health, dental, & vision
Company paid life insurance
401k savings
Company paid disability insurance
Equity program
Flexible work arrangements

Risks

Rapid AI development may outpace TetraScience's integration capabilities, risking obsolescence.
Dependency on partners like Google Cloud and NVIDIA could pose risks if disrupted.
International expansion may expose TetraScience to regulatory and compliance challenges.

Differentiation

TetraScience offers a vendor-neutral, open, cloud-native platform for scientific data management.
The platform integrates with any lab equipment or software, enhancing flexibility and adaptability.
TetraScience's Scientific Data Cloud centralizes and harmonizes data, preparing it for AI/ML applications.

Upsides

Partnerships with NVIDIA and Google Cloud enhance AI-native scientific datasets and capabilities.
Collaboration with Databricks accelerates the Scientific AI revolution in life sciences.
Bayer AG partnership maximizes scientific data value, driving innovation in biopharma.

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