We are seeking an exceptional Software Engineer to join our Applied AI team full-time. In this role, you will design, build, and deploy intelligent systems that move beyond research into production at scale. You’ll focus on architecting and evaluating multi-agent systems, retrieval-augmented generation (RAG) pipelines, and fine-tuned large language models—delivering AI capabilities that drive measurable business impact.
What You Will Do:
- Build at the frontier: Design and implement end-to-end AI systems, including multi-agent workflows, retrieval pipelines, and customized LLMs.
- Engineer full-stack solutions: Deliver web and backend applications that seamlessly integrate AI, ensuring reliability, scalability, and strong user experience.
- Raise the bar on evaluation: Develop rigorous truth sets, automated quality checks, and real-time monitoring pipelines to quantify performance and business outcomes.
- Prototype rapidly: Transform research concepts into production-grade systems through fast iteration, disciplined testing, and continuous refinement.
- Shape best practices: Contribute to internal standards for applied AI development, evaluation, and deployment at scale.
What You Will Need:
Education and Experience
- Bachelor’s degree in computer science, Engineering, or a related field
- + years of software development experience, including + years building production-grade AI systems
- Proven track record delivering AI agents, RAG pipelines, or fine-tuned models with measurable business impact
- Experience designing evaluation frameworks and truth sets for applied AI quality assurance
Knowledge, Skills, and Abilities
- Strong expertise in agent frameworks and LLM orchestration (API-first development, Vercel AI SDK, LangChain, etc.)
- Deep knowledge of RAG architectures, embeddings, vector databases, and retrieval optimization strategies
- Experience with LLM fine-tuning, prompt design, and model performance evaluation
- Full-stack engineering skills across modern web and backend technologies
- Familiarity with MLOps practices: CI/CD, model versioning, monitoring, and deployment at scale
- Strong grounding in applied information retrieval and vector-based systems
Salary:
$k - $k
Reporting Relationships:
This role reports to a senior leader (manager, director, or executive) and carries no direct reports.