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

Requirements

Candidates should have 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 is required. Strong knowledge of AWS and infrastructure-as-code frameworks, ideally with CDK, is necessary. Expert-level coding skills in TypeScript and Python building robust APIs and backend services are essential. Production-level experience with Databricks MLFlow, including model registration, versioning, asset bundles, and model serving workflows, is required. Expert level understanding of containerization (Docker) and hands-on experience with CI/CD pipelines and 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 is needed. The ability to articulate ideas clearly, present findings persuasively, and build rapport with clients and team members is also required.

Responsibilities

The Senior AI Infrastructure Engineer will 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. They will build and manage scalable data pipelines to ingest, transform, and serve data for ML and analytics. Responsibilities include developing infrastructure-as-code using tools like Cloudformation and AWS CDK to ensure repeatable and secure deployments. The engineer will collaborate with AI engineers, data engineers, and platform teams to improve the performance, reliability, and cost-efficiency of AI models in production. Driving best practices for observability, including monitoring, alerting, and logging for AI platforms, is a key duty. The role also involves contributing to the design and evolution of the AI platform to support new ML frameworks, workflows, and data types, and staying current with new tools and technologies to recommend improvements to architecture and operations. Integrating AI models and large language models (LLMs) into production systems to enable use cases using architectures like retrieval-augmented generation (RAG) is also part of the role.

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.

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