TetraScience

Senior AI Infrastructure Engineer

United States

Not SpecifiedCompensation
Senior (5 to 8 years), Expert & Leadership (9+ years)Experience Level
Full TimeJob Type
UnknownVisa
Scientific Data, AI Cloud, Life Sciences, BiotechnologyIndustries

Senior AI Infrastructure Engineer

Salary: Employment Type: Location Type: Remote

Position Overview

TetraScience is seeking a Senior AI Infrastructure Engineer to join our team. This role is crucial for designing, building, and scaling our AI and data infrastructure. You will focus on architecting and maintaining cloud-based MLOps pipelines to enable scalable, reliable, and production-grade AI/ML workflows. You will collaborate closely with AI engineers, data engineers, and platform teams, leveraging your expertise in building and operating modern cloud-native infrastructure to empower world-class AI capabilities across the organization. If you are passionate about building robust AI infrastructure, enabling rapid experimentation, and supporting production-scale AI workloads, we encourage you to apply.

Responsibilities

  • Design, implement, and maintain cloud-native infrastructure to support AI and data workloads, with a focus on AI and data platforms such as Databricks and AWS Bedrock.
  • Build and manage scalable data pipelines to ingest, transform, and serve data for ML and analytics.
  • Develop infrastructure-as-code using tools like Cloudformation, AWS CDK to ensure repeatable and secure deployments.
  • Collaborate with AI engineers, data engineers, and platform teams to improve the performance, reliability, and cost-efficiency of AI models in production.
  • Drive best practices for observability, including monitoring, alerting, and logging for AI platforms.
  • Contribute to the design and evolution of our AI platform to support new ML frameworks, workflows, and data types.
  • Stay current with new tools and technologies to recommend improvements to architecture and operations.
  • Integrate AI models and large language models (LLMs) into production systems to enable use cases using architectures like retrieval-augmented generation (RAG).

Requirements

  • 7+ years of professional experience in software engineering and infrastructure engineering.
  • Extensive experience building and maintaining AI/ML infrastructure in production, including model, deployment, and lifecycle management.
  • Strong knowledge of AWS and infrastructure-as-code frameworks, ideally with CDK.
  • Expert-level coding skills in TypeScript and Python building robust APIs and backend services.
  • Production-level experience with Databricks MLFlow, including model registration, versioning, asset bundles, and model serving workflows.
  • Expert level understanding of containerization (Docker).
  • Hands-on experience with CI/CD pipelines.
  • Experience with orchestration tools (e.g., ECS) is a plus.
  • Proven ability to design reliable, secure, and scalable infrastructure for both real-time and batch ML workloads.
  • Ability to articulate ideas clearly, present findings persuasively, and build rapport with clients and team members.

Company Information

Who We Are 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.

The Tetra Way 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

AI Infrastructure
MLOps
Cloud-based infrastructure
Scalable AI/ML workflows
Data infrastructure
Cloud-native infrastructure
AI engineering
Data engineering
Platform engineering

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.

Land your dream remote job 3x faster with AI