Lead Python Engineer — HPC & Scientific Computing

Quantori; United States; Hybrid; USD 140,000 - 210,000; Full time

Benefits offered by this job

Competitive compensation

Remote or office work

Continuous education, mentoring, and专业

Job summary

Quantori is seeking a Lead Python Engineer in the San Francisco Bay Area to collaborate with scientists on addressing pain points, developing features, integrating systems, and advancing scientific software. The role blends hands-on coding with HPC workflows in a hybrid work model (2–3 days on site).

Key tasks include building scalable Python backend services, optimizing data processing pipelines, and leveraging AWS and Docker to support research workloads.

Qualifications

• Pharma/Biotech R&D application experience.
• Strong Python development experience in scientific computing or data-intensive applications.
• Hands-on Scientific Computing and HPC experience, including Slurm, shared filesystems, and batch processing.
• Strong Linux, shell scripting, scientific package and environment management skills.
• Experience developing and troubleshooting scientific data processing and analysis pipelines.
• AWS Cloud experience and Docker.
• REST APIs, microservices, and Python backend frameworks (Django/FastAPI or similar).
• SQL and relational databases.
• Experience with AI-assisted software development tools.
• Strong communication skills and ability to work directly with scientists to understand scientific workflows and resolve technical issues.

Skills

Python

Linux

REST APIs

Microservices

Django/FastAPI

SQL

AI-assisted tools

Team communication

Tools

Docker

AWS

Slurm

Shared filesystems

Job description

Quantori is seeking a Lead Python Engineer in the San Francisco Bay Area to collaborate with scientists on addressing pain points, developing features, integrating systems, and advancing scientific software. The role blends hands-on coding with HPC workflows in a hybrid work model (2–3 days on site).

Key tasks include building scalable Python backend services, optimizing data processing pipelines, and leveraging AWS and Docker to support research workloads.