Applied AI Engineer

ArtosAI•San Francisco, CA

33d•$171,000 - $242,000•Onsite

About The Position

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.

Requirements

Bachelor's or Master's degree in Computer Science, Engineering, or a related field (or equivalent practical experience)
2+ years of software development experience building and deploying AI/ML applications
Hands-on experience building LLM-based applications
Experience working with frontier models (e.g., OpenAI, Anthropic, Google)
Experience with AI tools as a user, specifically AI code editors
Use of evaluation tools such as Langfuse or LangSmith
Strong backend engineering experience
Ability to stay current with emerging practices, models, and tooling in the generative AI ecosystem and apply them pragmatically.

Nice To Haves

Worked with Infrastructure-as-Code tools such as Terraform or Pulumi
Implementing CI/CD pipelines (e.g., GitHub Actions)
Experience working in or adjacent to regulated domains (life sciences, clinical R&D) is a plus
Frontend development experience (e.g., React) is a plus, but not required

Responsibilities

Developing, scaling, and expanding the Artos platform to serve regulatory needs for pharma and life science companies around the globe.
Designing multi-step LLM workflows and task-specific agents.
Developing advanced prompt engineering strategies, evaluation frameworks, and RAG pipelines.
Conducting technical R&D to explore and define the boundaries of model functionality.
Building APIs from the ground up using Python frameworks such as FastAPI and Django.
Deploying and scaling containerized applications in cloud environments (e.g., AWS, GCP, Azure).
Ability to design and maintain scalable, production-grade backend systems for AI applications.
Ability to create, orchestrate, and evaluate LLM-based agents and chained workflows with minimal oversight.
Ability to implement and orchestrate multi-step agentic workflows.
Ability to debug and improve LLM-driven systems, identifying issues across multiple layers (model output, API behavior, system logic).
Ability to conduct rapid experimentation and research on LLM capabilities and translate findings into production functionality.
Ability to stay current with emerging practices, models, and tooling in the generative AI ecosystem and apply them pragmatically.
Ability to communicate clearly with technical and non-technical collaborators (e.g., product managers, medical writers, customer teams).
Ability to operate effectively in a fast-paced, ambiguity-heavy environment, managing shifting priorities and novel problem spaces.