Applied AI Engineer

Posted 8 Hours Ago
San Francisco, CA, USA
Hybrid
171K-242K Annually
Junior
Artificial Intelligence • Software • Biotech • Generative AI
The Role
Build and scale production-grade backend systems and APIs for LLM-based applications. Design and orchestrate multi-step LLM agents and RAG pipelines, perform prompt engineering and evaluation, run technical R&D on model capabilities, deploy containerized services to cloud, and collaborate with product and domain teams in a fast-paced startup.
Summary Generated by Built In
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

Skills Required

  • Bachelor's or Master's degree in Computer Science, Engineering, or related field (or equivalent experience)
  • 2+ years 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 prompt engineering strategies, evaluation frameworks, and RAG pipelines
  • Conducting technical R&D to define model functionality boundaries
  • Use of evaluation tools such as Langfuse or LangSmith
  • Building APIs from the ground up using Python frameworks such as FastAPI and Django
  • Deploying and scaling containerized applications in cloud environments (AWS, GCP, Azure)
  • Design and maintain scalable, production-grade backend systems for AI applications
  • Create, orchestrate, and evaluate LLM-based agents and chained workflows with minimal oversight
  • Implement and orchestrate multi-step agentic workflows
  • Debug and improve LLM-driven systems across model, API, and system layers
  • Conduct rapid experimentation and translate LLM research into production functionality
  • Stay current with emerging generative AI practices, models, and tooling
  • Communicate clearly with technical and non-technical collaborators
  • Operate effectively in a fast-paced, ambiguity-heavy startup environment
  • Worked with Infrastructure-as-Code tools such as Terraform or Pulumi
  • Implemented CI/CD pipelines (e.g., GitHub Actions)
  • Experience in regulated domains (life sciences, clinical R&D)
  • Frontend development experience (e.g., React)
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The Company
21 Employees
Year Founded: 2023

What We Do

Artos AI develops a generative-AI platform for biopharma and life-sciences organizations. Its software helps clinical development, medical writing, CMC, and regulatory teams create, manage, trace, and collaborate on documents used in regulatory submissions, including INDs, NDAs, and BLAs. By turning structured and unstructured data into submission-ready materials, Artos aims to shorten development timelines and bring treatments to patients faster and more efficiently.

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