Applied AI Engineer

About the role

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:

- 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

- Designing multi-step LLM workflows and task-specific agents

- Experience working with frontier models (e.g., OpenAI, Anthropic, Google)

- Experience with AI tools as a user, specifically AI code editors

- Developing advanced prompt engineering strategies, evaluation frameworks, and RAG pipelines

- Conducting technical R&D to explore and define the boundaries of model functionality

- Use of evaluation tools such as Langfuse or LangSmith

- Strong backend engineering experience, including:

- 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)

Requirements:

- 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

Nice to Have:

- 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

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 Schrödinger’s cat waiting to be observed, and knows too many random facts about the Mongol postal system