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Backend Software Engineer

Hybrid
Artos AISan Francisco, CA, US23 hours agoWebsite
Fresh
Full-time
Engineering

Compensation

$166,500-$231,000
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Description

About Artos:

At Artos, we build tools that help biopharma companies create and manage their R&D documentation in a fraction of the time. If you’re looking to join a team whose mission is to fundamentally change the way that drug development gets done, we’d love to talk to you.

About the Role:

We're growing fast, and we're looking for an engineer who thrives in a high-velocity environment and wants to do meaningful work. At Artos, you'll help accelerate development of a platform that supports companies — from innovative biotech startups to the world's largest pharmaceutical firms — in delivering life-saving treatments to patients faster than ever before.

As a core member of Artos's engineering team, you'll play a critical role in developing, scaling, and expanding the Artos platform to serve regulatory needs for pharma and life science companies around the globe.

 

Qualifications:

  • BS/MS in Computer Science, Engineering, or related field (or equivalent experience)

  • 3+ years in software development with a focus on AI/ML applications

  • Hands-on experience with GenAI applications: prompt engineering, RAG, data pipelines, and eval frameworks

  • Experience building APIs with modern Python frameworks (FastAPI, Django, etc.)

  • Experience deploying and scaling containerized apps in cloud environments (AWS preferred)

  • Experience building CI/CD pipelines for production backend systems

  • Familiarity with secure coding practices, ideally from a regulated industry (fintech, life sciences)

  • Pluses: IaC (Terraform/Pulumi), React, knowledge of life sciences regulatory requirements

Requirements:

  • Design, build, and maintain scalable backend systems in production

  • Build APIs and services using Python frameworks (FastAPI, Django, etc.)

  • Work with containerized apps and cloud infrastructure (Docker, AWS, Terraform)

  • Implement CI/CD pipelines and debug production systems

  • Rapidly apply LLM techniques: prompt engineering, fine-tuning, RAG

  • Stay current with generative AI best practices and apply them pragmatically

  • Communicate technical decisions clearly to both technical and non-technical audiences

  • Collaborate across teams (product, medical writers, customer success)

  • Navigate ambiguous requirements and execute independently

  • Debug across system layers: application logic, model behavior, APIs, infrastructure

Other Information:

Very comfortable working in a fast-paced and intense startup environment

Willing to work in-person in our office in Mission Bay 4-5 days/week

Likes matcha KitKats, believes every LLM prompt is just Schrodinger’s cat waiting to be observed, and knows too many random facts about the Mongol postal system

Stack

PythonLLMsGenerative AIAWSTerraformCI/CDReactMachine LearningFine-tuningDockerRAGPrompt EngineeringData Engineering
Posted
Unknown
Last seen
Jun 26, 2026
First seen
Jun 26, 2026
Status
active