Senior Forward-Deployed Engineer, Life Sciences - Remote

SandboxAQ

Northern (KY)

Hybrid

USD 140,000 - 210,000

Full time

8 days ago

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Job summary

SandboxAQ is hiring a Staff Forward Deployed Engineer for its AI Simulation group. You will be the technical bridge between Large Quantitative Models and client contracts, operating with autonomy to create solutions that drive drug discovery and chemical simulation initiatives.

You will embed with scientific delivery teams, own deployments, and lead end-to-end technical execution, from ideation to production, leveraging Python, Kubernetes, Airflow, and Terraform in a cloud environment.

Qualifications

  • 7+ years of professional software development experience.
  • Deep proficiency in Python and modern architectural patterns.
  • Hands-on experience with cloud orchestration (Kubernetes, Batch systems) and workflow tools like Airflow.
  • Technical expertise in building REST APIs, managing databases and using Infrastructure as Code (Terraform).
  • Entrepreneurial mindset with track record of shipping in fast-moving environments.

Responsibilities

  • Orchestrate technical delivery with scientific teams for end-to-end execution of client contracts.
  • Architect and develop high-velocity code and frameworks wrapping scientific logic for R&D workflows.
  • Drive cross-functional collaboration between researchers, product teams, and engineering.
  • Scale and optimize cloud-based compute resources and data pipelines across contract lifecycles.
  • Lead integration of core technologies like LQMs and Agentic systems into client environments.

Skills

Python
Kubernetes
Airflow
REST APIs
Databases/ORM
Cloud computing
Terraform

Tools

Kubernetes
Airflow
Terraform

Job description

SandboxAQ is hiring a Staff Forward Deployed Engineer for its AI Simulation group. You will be the technical bridge between Large Quantitative Models and client contracts, operating with autonomy to create solutions that drive drug discovery and chemical simulation initiatives.

You will embed with scientific delivery teams, own deployments, and lead end-to-end technical execution, from ideation to production, leveraging Python, Kubernetes, Airflow, and Terraform in a cloud environment.

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