TetraScience

Lead Platform Engineer

Boston, Massachusetts, United States

Not SpecifiedCompensation
Expert & Leadership (9+ years)Experience Level
Full TimeJob Type
UnknownVisa
Scientific Data, AI Cloud, Data ManagementIndustries

Job Description: Lead Platform Engineer

Location Type: Remote


Position Overview

TetraScience is seeking a Lead Platform Engineer to play a critical role in evolving and scaling our cloud-native Tetra data platform. This role is crucial for handling 100x growth in data volume and user demand. You will partner with engineering, data, and AI teams to design scalable architectures, proactively anticipate and mitigate scaling challenges, and ensure our platform remains performant, reliable, and cost-efficient.

This is a highly impactful role for an engineer passionate about distributed systems, understanding the trade-offs of large-scale design, and thriving on turning ambitious scalability goals into concrete technical strategies. If you are excited by the challenge of architecting cloud-native infrastructure to power massive growth and solving complex scalability problems, we encourage you to apply.


Responsibilities

  • Architect and evolve our cloud-native platform infrastructure to support high-throughput, low-latency data processing patterns, customer-facing features, and design platform to meet scalability requirements.
  • Design scalable, distributed systems powering complex capabilities such as authentication & authorization, data lifecycle management, search infrastructure, operational intelligence, and real-time event processing.
  • Proactively analyze platform performance and scalability; identify potential constraints and define strategies that enable both continuous and step-function growth.
  • Collaborate with engineering and product teams to deliver infrastructure that supports new services, customer-facing applications, and high-volume data processing workloads.
  • Build and maintain infrastructure-as-code (e.g., CloudFormation, AWS CDK) to automate, standardize, and secure deployments, supporting online upgrades and on-demand infrastructure allocation.
  • Enhance observability and monitoring to ensure reliability, cost efficiency, and rapid incident response.
  • Champion best practices in distributed systems design, scalability, and performance optimization, and share architectural insights through design reviews and technical documentation.

Requirements

  • 10+ years of hands-on experience in software and infrastructure engineering, with a proven track record of designing, building, and scaling distributed, cloud-native systems in production environments.
  • Demonstrated experience as a technical leader or architect, making key decisions on system design, scalability, performance, and cost optimization.
  • Strong proficiency in API-first design, including REST, GraphQL, and OpenAPI specifications, designing APIs that are scalable, secure, versioned, and extensible.
  • Strong proficiency in TypeScript and Python.

Company Information

TetraScience is the Scientific Data and AI Cloud company. We are catalyzing the Scientific AI revolution by designing and industrializing AI-native scientific data sets, which we bring to life in a growing suite of next-gen lab data management solutions, scientific use cases, and AI-enabled outcomes. TetraScience is the category leader in this vital new market, generating more revenue than all other companies in the aggregate. In the last year alone, the world’s dominant players in compute, cloud, data, and AI infrastructure have converged on TetraScience as the de facto standard, entering into co-innovation and go-to-market partnerships.

Note: In connection with your candidacy, you will be asked to carefully review the Tetra Way letter, authored directly by Patrick Grady, our co-founder and CEO. This letter is designed to assist you in better understanding whether TetraScience is the right fit for you from a values and ethos perspective. It is impossible to overstate the importance of this document, and you are encouraged to take it literally and reflect on whether you are aligned with our unique approach to company and team building. If you join us, you will be expected to embody its contents each day.

Skills

Platform Engineering
Cloud-native architecture
Scalability
Distributed systems
Data platform
AI
Cost-efficiency
Performance optimization
Reliability

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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