The Role
Own the cloud infrastructure, CI/CD systems, and deployment automation for TetraScience’s multi-tenant SaaS platform serving global biopharma customers. This is a hands-on technical lead role. You will lead through technical depth and influence across teams. Strong architecture and implementation skills are important for success in this role. You will evolve our cloud architecture, build substantial parts of it in Python, CloudFormation and Terraform. You will architect and build deployment pipelines to AWS and Databricks, and drive the engineering practices that determine how fast and safely we ship software.
What You’ll Own
Infrastructure as Code
Own, design, build, and maintain the cloud infrastructure using Cloudformation, Terraform and custom Python glue.. Every environment is provisioned and governed through code. Architect the deployment pipeline infrastructure end to end. GitHub Actions, CodeBuild, container image pipelines, code scanning, artifact registries, pre-merge integration environments, promotion gates, and automated rollback. Your goal: engineers merge code and it reaches production safely without manual intervention. You will partner with other engineering teams to reduce cycle time from commit to production. Instrument pipeline metrics (build time, deployment frequency, change failure rate, MTTR). Identify and eliminate bottlenecks. Build self-service capabilities so product teams are not blocked by infrastructure.
Cloud Engineering
Deep, hands-on AWS experience: Serverless Architecture, EKS/ECS, VPC/networking, IAM, KMS, CloudWatch, Lambda, S3, EC2, Kinesis, Athena, Glue, CloudTrail, CostExplorer. You understand Well-Architected Framework principles and apply them daily, not as a checklist exercise.
Databricks experience is strongly preferred.
DevSecOps Embed security into the product and pipelins: container image scanning, SAST/DAST integration, secrets management, least-privilege IAM, and compliance-as-code. You work in a GxP-regulated environment where auditability and traceability of deployments are non-negotiable.
Observability and Reliability
Production monitoring, alerting, log aggregation, and incident response infrastructure. Support for developer teams. Blameless postmortem culture.
Current Tech Stack
- Cloud: AWS
- IaC: Terraform, CloudFormation
- CI/CD: GitHub Actions
- Containers: Docker, ECS
- Languages: Python, Bash
- Data: PostgreSQL / Aurora, S3 data lake, Databricks (Lakehouse)
Why This Role Matters
TetraScience is building the data and AI platform for drug development. Our customers are global pharma companies running regulated scientific workloads. The infrastructure you build determines whether we ship features weekly or monthly, whether customer environments are secure and compliant by default, and whether the platform scales from tens to hundreds of enterprise deployments. Release velocity is a company-level strategic priority, and this role is at the center of it.
What We Are Not Looking For
To save everyone’s time: this role is not for traditional IT operations. If your background is primarily in manual server provisioning, ticketing-system-driven change management, desktop support, or on-prem datacenter administration, if you always deploy someone else’s code via IaC, this is not the right fit. We need someone whose default mode is writing code to solve infrastructure problems.
Requirements
Required Experience
- 7+ years in DevOps, Cloud Engineering, or Platform Engineering roles, with at least 2 years in a senior or lead capacity
- Deep, daily-driver coding experience:,programmatically managing infrastructure through Python, APIs and IaC tools is second nature to you. The web console is an afterthought.
- Strong production AWS experience: compute (EKS, ECS, EC2), networking (VPC, Transit Gateway, ALB/NLB, Route53), storage (S3, EBS, EFS), security (IAM, KMS, Security Hub, GuardDuty)
- Designed and built CI/CD pipeline infrastructure (not just consumed existing pipelines). GitHub Actions, GitLab CI, or Jenkins at scale.
- Container orchestration: ECS, Docker, Kubernetes (EKS preferred), service mesh concepts
- Scripting and automation: Python or Go. Bash only is not enough
- Git-based workflows, branch strategies, and pull-request-driven infrastructure changes
- Experience designing and operating resilient and scalable cloud solutions
- Experience operating in a regulated or compliance-sensitive environment (GxP, SOC2, HIPAA, FedRAMP, or similar)
Strong Preferences
- AWS. Databricks. Azure and GCP are nice to have.
- AWS Solution Architect Professional or equivalent certification
- Managed database services: Aurora, RDS, Redshift, DynamoDB, or Databricks infrastructure provisioning
- Big data infrastructure: Data lake storage architectures, Lakehouse, ETL pipeline infrastructure
- Cost optimization: resource tagging strategies, FinOps practices in aws
- Policy-as-code: OPA/Rego, Sentinel, or AWS SCPs for governance at scale
Benefits
- Competitive compensation with equity
- Unlimited PTO
- Company-paid Life Insurance, LTD/STD
- 401(k)
The salary range for this position is $150,000-$240,000. The salary range posted reflects our target baseline for this role. Final compensation is determined by a thorough evaluation of factors including the candidate’s specific experience, localized market data, and internal team equity.
Skills Required
- 7+ years in DevOps, Cloud Engineering, or Platform Engineering roles
- Deep, daily Terraform experience
- Strong production AWS experience
- Designed and built CI/CD pipeline infrastructure
- Container orchestration experience
- Scripting and automation experience
- Experience operating in a compliance-sensitive environment
What We Do
Biopharma R&D runs on data trapped in silos. Every instrument, ELN, and LIMS writes results in its own format, so most of what a scientist learns in one experiment never reaches the next. That's a big reason drug development costs keep climbing even as computing power gets cheaper. TetraScience builds Tetra OS, the operating system for scientific intelligence. It sits above and alongside a company's existing instruments, ELNs, and LIMS, works across any vendor, and turns raw scientific data into structured, AI-ready intelligence. Four components make it work. The Scientific Data Foundry ingests data from any source and converts it into governed, machine-readable data at scale. The Scientific Use Case Factory turns one-off workflows into reusable, validated ones deployed across sites. Tetra AI reasons and orchestrates on top of that foundation, grounded in real data instead of guessing. Sciborg teams, our embedded scientist-engineers, work inside customer organizations to get all of it adopted, not just installed. These parts compound. Better data makes workflows faster to build. Workflows generate better data. The result is intelligence that gets more valuable with use instead of resetting on every new project. Customers include Takeda, Bayer, Regeneron, Roche and and AstraZeneca, alongside partners like NVIDIA, Databricks, and Thermo Fisher Scientific. One top-20 pharma customer connected over 1,000 instruments, processed a petabyte of data in a year, and cut 20,000 hours of administrative work. Founded by Patrick Grady and Spin Wang, headquartered in Boston with a European base in Basel. Mission: radically improve and extend human life.
Why Work With Us
We're building the operating system underneath scientific R&D, not another point tool. Work touches real drug development at Takeda, Bayer, and Novartis. Every employee sees the same financials the board sees. We hire people who reason from first principles and run at hard problems.
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