Applied LLM Engineer
About The Position
Qualifications Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field (or equivalent practical experience) 3+ years of software development experience, with a focus on building and deploying AI/ML applications 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) Implementing CI/CD pipelines (e.g., GitHub Actions) Working with Infrastructure-as-Code tools such as Terraform or Pulumi Hands-on experience building LLM-based applications, including: Designing multi-step LLM workflows and task-specific agents Experience working with most frontier models (e.g. OpenAI, Anthropic, Google, etc…) 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 Familiarity with secure coding practices, ideally in regulated industries (e.g., life sciences, healthcare, fintech) 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 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 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 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
Requirements
- Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field (or equivalent practical experience)
- 3+ years of software development experience, with a focus on building and deploying AI/ML applications
- 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)
- Implementing CI/CD pipelines (e.g., GitHub Actions)
- Working with Infrastructure-as-Code tools such as Terraform or Pulumi
- Hands-on experience building LLM-based applications, including: Designing multi-step LLM workflows and task-specific agents
- Experience working with most frontier models (e.g. OpenAI, Anthropic, Google, etc…)
- 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
- Familiarity with secure coding practices, ideally in regulated industries (e.g., life sciences, healthcare, fintech)
Nice To Haves
- 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
- 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 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
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What This Job Offers
Job Type
Full-time
Career Level
Mid Level
